Papers with response generation
Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond (2023.acl-tutorials)
Copied to clipboard
| Challenge: | Conventional conversation researches focus on the responseability of the system, such as dialogue context understanding and response generation, but overlook the design of an essential property in intelligent conversations, i.e., goal awareness. |
| Approach: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
| Outcome: | This tutorial introduces the latest advances on the design of agent’s awareness of goals in a wide range of conversational systems. |
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing approaches to integrate the recommendation function and dialog generation function smoothly are lacking. |
| Approach: | They propose to integrate dialog context for recommendation and dialog generation better using a pre-trained language model and an item metadata encoder to integrate the recommendation and dialogue generation. |
| Outcome: | The proposed architecture improves the integration of recommendation and dialog generation functions. |
Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting (N19-2)
Copied to clipboard
| Challenge: | a recent study has focused on how algorithmic improvements help model performance on fabricated datasets. |
| Approach: | They propose two approaches to train conversational neural models for goal-oriented conversational systems . they train models on historical chat transcripts and test on live contacts . |
| Outcome: | The proposed model is able to generate top-four responses on live contacts . the model is also able for customer profile features to assess their impact on performance . |
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2020.acl-main)
Copied to clipboard
| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
| Approach: | They propose a dialogue generation pre-training framework that leverages bi-directional context and uni-directional characteristic of language generation. |
| Outcome: | The proposed framework is superior to existing models on three publicly available datasets. |
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
Copied to clipboard
| Challenge: | Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality. |
| Approach: | They propose a framework that leverages commonsense-based persona expansion to address such issues in long-term conversations. |
| Outcome: | The proposed framework facilitates better response generation via human-like persona refinement. |
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data (2021.acl-long)
Copied to clipboard
| Challenge: | Existing persona-based dialogue models use crowd-sourced data, such as the PersonaChat . however, the cost of such datasets is limited, and the model is not robust. |
| Approach: | They propose to disentangle persona-based dialogue generation into two sub-tasks by using a BERT-over-BERT model. |
| Outcome: | The proposed model outperforms baselines in response quality and persona consistency under different limited data settings. |
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)
Copied to clipboard
Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
| Challenge: | Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability. |
| Approach: | They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations . |
| Outcome: | The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient . |
FPI: Failure Point Isolation in Large-scale Conversational Assistants (2022.naacl-industry)
Copied to clipboard
| Challenge: | Large-scale conversational assistants can cause errors in their modules . a machine learning system can analyze large volumes of data and isolate the source of error . |
| Approach: | They propose a machine learning system that embeds incoming request and context using pre-trained transformer models and encodes additional metadata features to output failure point predictions. |
| Outcome: | The proposed system obtains 92.2% of human performance while scaling to analyze the entire traffic in 8 different languages of a large-scale conversational assistant. |
Retrieval Based Response Letter Generation For a Customer Care Setting (2022.naacl-industry)
Copied to clipboard
| Challenge: | Letter-like communications are a major means of customer relationship management . despite advances in natural language processing, the task of generating a response is time-consuming . |
| Approach: | They propose a deep-learning based response letter generation framework that uses data augmentation to retrieve knowledge from historical responses and utilize it to generate an appropriate response. |
| Outcome: | The proposed model outperforms baselines by significant margins while producing consistent and informative responses. |
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Recent work finds that realizing who holds the initiative can help select knowledge . however, there is a strong semantic transition between two rounds, probably leading to initiative misjudgment . |
| Approach: | They propose a topic-shift Aware Knowledge sElector(TAKE) model which locates relevant parts from dialogue history to improve knowledge selection. |
| Outcome: | The proposed model outperforms baseline models on the WoW. |
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling (P19-3)
Copied to clipboard
Vighnesh Leonardo Shiv, Chris Quirk, Anshuman Suri, Xiang Gao, Khuram Shahid, Nithya Govindarajan, Yizhe Zhang, Jianfeng Gao, Michel Galley, Chris Brockett, Tulasi Menon, Bill Dolan
| Challenge: | upcoming open-source natural language processing repository aims to train conversational agents for multi-turn situations. |
| Approach: | They present the Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) the framework wraps TensorFlow functionality in a modular component-based architecture. |
| Outcome: | The Intelligent Conversation Engine: Code and Pre-trained Systems (ICECAPS) is an open-source natural language processing repository. |
UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve reasoning abilities of Large Language Models (LLMs) have limitations due to excessive growth in context length, causing large hardware burden. |
| Approach: | They propose a novel Unified ICL framework that unifies demonstration compression, demonstration selection, and final response generation. |
| Outcome: | The proposed framework unifies demonstration compression, demonstration selection, and final response generation. |
Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022.acl-long)
Copied to clipboard
| Challenge: | Existing work on empathetic dialogues focused on the two-party scenario, but multi-party dialogues are pervasive in reality. |
| Approach: | They propose a multi-party empathetic dialogue generation task that uses a static-dynamic model to explore emotion and sensibility. |
| Outcome: | The proposed task is based on a model with static sensibility and dynamic emotion . it achieves state-of-the-art performance in multi-party empathetic dialogue learning . |
Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)
Copied to clipboard
| Challenge: | Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up . |
| Approach: | They propose to generate entity autoregressively before leveraging it to guide response generation. |
| Outcome: | Experiments on MultiWOZ 2.1 single and CAMREST show that the proposed system generates more high-quality and entity-consistent responses in an end-to-end manner. |
InSCIt: Information-Seeking Conversations with Mixed-Initiative Interactions (2023.tacl-1)
Copied to clipboard
Zeqiu Wu, Ryu Parish, Hao Cheng, Sewon Min, Prithviraj Ammanabrolu, Mari Ostendorf, Hannaneh Hajishirzi
| Challenge: | In information-seeking conversations, a user may ask questions that are under-specified or unanswerable. |
| Approach: | They present a dataset for information-seeking conversations with mixed-initiative interactions . they use Wikipedia to search for answers and provide relevant information . |
| Outcome: | The proposed system significantly underperforms humans in two of the most recent studies. |
Explicit Use of Topicality in Dialogue Response Generation (2022.naacl-srw)
Copied to clipboard
| Challenge: | Existing chat dialogue systems only implicitly consider the topic given the context, but not explicitly. |
| Approach: | They propose a dialogue system that responds appropriately following the topic by selecting the entity with the highest “topicality” they define the entity as a noun or compound nouns, and topicality as the degree of speaker awareness directed toward each entity in the dialogue context. |
| Outcome: | The proposed system can follow the topic more than existing systems that only consider the context . |
Muffin: Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have shown that emotional support conversation models generate unhelpful responses that can hinder their effectiveness. |
| Approach: | They propose a model-agnostic framework called Mitigating unhelpfulness with multifaceted AI feedback for emot io nal support (Muffin) it uses a multifaceted feedback module to assess helpfulness model responses across various facets of emotional support and contrasts helpful and unhelpful responses generated by the model. |
| Outcome: | The proposed framework reduces the likelihood of unhelpful responses by comparing helpful and unhelpfully responses generated by previous models to improve response fluency and relevance. |
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation (2024.acl-srw)
Copied to clipboard
| Challenge: | Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. Obtaining a large volume of expert-annotated data is costly for most tasks. |
| Approach: | They propose a method that trains the model to prioritize the best responses from a pool of candidates created for a task using ranking metrics. |
| Outcome: | The proposed method is more robust, less sensitive to noise, and can be achieved with limited human annotations or through heuristic methods. |
Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues (2024.findings-eacl)
Copied to clipboard
| Challenge: | blending multiple languages within a single conversation presents a formidable challenge, given the wide-ranging variations influenced by individual speaking styles and cultural backgrounds. |
| Approach: | They propose a novel approach to harness the Big Five personality traits acquired in an unsupervised manner from code-mixed conversations to bolster the performance of response generation. |
| Outcome: | The proposed approach enhances contextual relevance and performance of the proposed model by combining personality traits with dialogue context. |
Improving Factual Consistency Between a Response and Persona Facts (2021.eacl-main)
Copied to clipboard
| Challenge: | Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with persona facts. |
| Approach: | They propose to fine-tune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility. |
| Outcome: | The proposed model improves the rate of responses that are factually consistent with persona facts over its supervised counterpart while maintaining the language quality of responses. |
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to empathetic response generation only consider causalities between the user’s emotion and the user's experiences and neglect interdependence among causalities and reason them independently. |
| Approach: | They propose to use a conditional variable Graph Auto-Encoder to reason all plausible causalities interdependently and simultaneously given the user’s emotion, dialogue history, and future dialogue content. |
| Outcome: | The proposed method achieves state-of-the-art in a real-world situation. |
Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations (2025.coling-main)
Copied to clipboard
| Challenge: | Existing retrieval-based methods for long-term conversations face challenges in memory database management and accurate memory retrieval, hindering their efficacy in dynamic, real-world interactions. |
| Approach: | They propose a framework that eschews traditional retrieval modules and memory databases and adopts a “One-for-All” approach to manage memory generation, compression, and response generation. |
| Outcome: | The proposed framework produces more nuanced and human-like experiences than retrieval-based methods. |
Advanced Messaging Platform (AMP): Pipeline for Automated Enterprise Email Processing (2025.acl-industry)
Copied to clipboard
Simerjot Kaur, Charese Smiley, Keshav Ramani, Elena Kochkina, Mathieu Sibue, Samuel Mensah, Pietro Totis, Cecilia Tilli, Toyin Aguda, Daniel Borrajo, Manuela Veloso
| Challenge: | a lack of publicly available datasets for training and benchmarking limits current AI techniques' effectiveness in industry-specific applications. |
| Approach: | They propose an email automation pipeline that automates email response generation at scale in real-world enterprise settings. |
| Outcome: | The proposed pipeline automates email response generation at scale in real-world environments. |
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Knowledge-based open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. |
| Approach: | They propose a benchmark for evaluating multi-source dialogue knowledge selection and response generation using Wikipedia's wizard of Wikipedia. |
| Outcome: | The proposed benchmark is called multi-source Wizard of Wikipedia (Ms.WoW) it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. |
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)
Copied to clipboard
| Challenge: | Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent. |
| Approach: | They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target. |
| Outcome: | The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses. |
Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach (2024.emnlp-industry)
Copied to clipboard
| Challenge: | a breastfeeding and maternity expert is a scarce resource and engaging in a conversation with mothers is time-consuming. |
| Approach: | They propose to use RAG to allow experts to draft their responses from a generated text or to answer from scratch. |
| Outcome: | The proposed solution is based on a RAG approach to response generation . experts can draft their response from the generated text or answer from scratch . |
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models (2024.naacl-long)
Copied to clipboard
Yi Luo, Zhenghao Lin, YuHao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong
| Challenge: | Guide-Align is a guideline-oriented approach to augment the safety and quality of Large Language Models. |
| Approach: | They propose a guideline-oriented method to augment the safety and quality of large language models. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks and shows significant improvements in security and quality. |
UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue (2022.acl-short)
Copied to clipboard
| Challenge: | Existing studies tackle the problem of error propagation by decomposing the goal-oriented document-grounded dialogue into two sub-tasks. |
| Approach: | They propose to unify knowledge identification and response generation into two sub-tasks by sequentially generating grounding knowledge and response. |
| Outcome: | The proposed framework unifies knowledge identification and response generation and models their characteristics using a prompt-connected multi-task learning strategy. |
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness (2020.emnlp-main)
Copied to clipboard
| Challenge: | Large-scale dialogue datasets contain a non-negligible number of unacceptable utterance pairs . previous studies have identified such flaws and reported that the corpus is noisy . |
| Approach: | They propose a method for scoring the quality of utterance pairs based on their connectivity and relatedness. |
| Outcome: | The proposed method has a good correlation with human judgment of dialogue quality and is applied to training data filtered by the proposed method. |
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation (2022.acl-long)
Copied to clipboard
| Challenge: | generative dialogue models use dialogue histories to generate the response . however, generating a response based on the historical information is not easy . |
| Approach: | They propose a framework that utilizes simulated dialogue futures to enhance response generation. |
| Outcome: | The proposed framework can generate better responses over strong baselines on two open-domain dialogue datasets. |
CachePrune: Teaching LLMs What Not to Follow via KV-Cache Editing (2026.acl-long)
Copied to clipboard
Rui Wang, Junda Wu, Yu Xia, Tong Yu, Ruiyi Zhang, Ryan A. Rossi, Subrata Mitra, Lina Yao, Julian McAuley
| Challenge: | Existing Large Language Models exhibit critical vulnerability to indirect prompt injection attacks, where instructions injected within in the prompt context can override the user's intent. |
| Approach: | They propose a neural pruning algorithm that prunes neurons associated with instruction-following during KV cache encoding of the prompt context. |
| Outcome: | The proposed approach significantly reduces the attack success rate while preserving the model's ability to follow user instructions. |
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to empathetic response generation ignore the emotion cause . existing dialogue systems lack emotion understanding and empathy . |
| Approach: | They propose a framework that integrates emotion cause information into empathetic response generation by predicting context emotion labels and sequence of emotion cause-oriented labels. |
| Outcome: | The proposed framework improves empathetic response generation by incorporating emotion cause information into the model. |
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation (2025.findings-naacl)
Copied to clipboard
Yiruo Cheng, Kelong Mao, Ziliang Zhao, Guanting Dong, Hongjin Qian, Yongkang Wu, Tetsuya Sakai, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing research focuses on single-turn RAG, leaving a gap in addressing multi-turn conversations . a new benchmark is designed to assess RAG systems in realistic multi-turned conversations based on Wikipedia . |
| Approach: | They propose a large-scale benchmark to assess RAG systems in multi-turn contexts . CORAL includes diverse information-seeking conversations automatically derived from Wikipedia . authors propose unified framework to standardize various conversational RAG methods . |
| Outcome: | The proposed framework supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. |
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation (C18-1)
Copied to clipboard
| Challenge: | Existing models for conversation systems operate sentences at word-level . word-based models suffer from Unknown Words Issue and Preference Issue . |
| Approach: | They propose a hybrid-level Encoder-Decoder model which utilizes word-level features and character-level ones. |
| Outcome: | The proposed model outperforms non-word-level models in automatic metrics and human annotations on a Chinese corpus. |
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information (D18-1)
Copied to clipboard
| Challenge: | Existing work on how to generate relevant and informative responses is focusing on how dialogue systems generate information from large dialogue corpus. |
| Approach: | They propose to use dialogue corpus to generate relevant responses by using prototypes to extract semantic information from PMN. |
| Outcome: | The proposed model outperforms classical and strong baseline models in generating relevant and informative responses. |
Unsupervised Slot Schema Induction for Task-oriented Dialog (2022.naacl-main)
Copied to clipboard
| Challenge: | Defining task-specific schemas is the first step of building a task-oriented dialog system. |
| Approach: | They propose an unsupervised approach for slot schema induction from unlabeled dialog corpora using in-domain language models and unsupervised parsing structures. |
| Outcome: | The proposed method shows significant performance improvement on multi-domain and SGD datasets. |
Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing topic-grounded dialogue systems focus on the current conversation and corresponding topic sequence to predict the next topic. |
| Approach: | They propose a new approach to exploit topic-guided conversations to better model post-to-response topic-transition and guide the response generation to the current conversation. |
| Outcome: | The proposed model outperforms baselines on prediction and generation tasks. |
Beyond Verbal Cues: Emotional Contagion Graph Network for Causal Emotion Entailment (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on identifying the causes of emotions by understanding verbal contextual utterances, but this study often lacks recognizing the underlying emotional stimuli present in these utterrances. |
| Approach: | They propose an Emotional Contagion Graph Network that simulates the impact of non-verbal emotional cues on the counterpart’s emotions. |
| Outcome: | The proposed model is compared with state-of-the-art models on a benchmark dataset and the results are encouraging. |
A Large-Scale Dataset for Empathetic Response Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing empathetic datasets are limited in size and cost due to the cost of manual labor. |
| Approach: | They propose to annotate 1M dialogues with 32 fine-grained emotions and eight empathetic response intents and the Neutral category using a silver dataset. |
| Outcome: | The proposed pipeline compares the quality of the proposed dataset with a state-of-the-art gold dataset using offline experiments and visual validation methods. |
Situated and Interactive Multimodal Conversations (2020.coling-main)
Copied to clipboard
Seungwhan Moon, Satwik Kottur, Paul Crook, Ankita De, Shivani Poddar, Theodore Levin, David Whitney, Daniel Difranco, Ahmad Beirami, Eunjoon Cho, Rajen Subba, Alborz Geramifard
| Challenge: | Situated Interactive MultiModal Conversations (SIMMC) is a new direction for virtual assistants that handle multimodal inputs and perform multimodal actions. |
| Approach: | They propose to use Situated Interactive MultiModal Conversations (SIMMC) to train agents to take multimodal actions grounded in a co-evolving multimodal context. |
| Outcome: | The proposed model will be made publicly available. |
RevCore: Review-Augmented Conversational Recommendation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing conversational recommendation systems lack item information when conducted on short dialogue history and unfamiliar items. |
| Approach: | They propose a framework where reviews are seamlessly incorporated into conversational recommendation systems. |
| Outcome: | The proposed framework yields better performance on recommendation and conversation responding. |
Compact Language Models with Iterative Text Refinement for Health Dialogue Summarization (2026.eacl-long)
Copied to clipboard
| Challenge: | Health wellness agents typically rely on large language models (LLMs) for response generation, where contextual information from prior conversations can be utilized for response grounding and personalization. |
| Approach: | They propose to use large language models to generate high-quality health dialogue summaries by using iterative feedback. |
| Outcome: | The proposed method outperforms baseline on open-source and proprietary benchmarks and can run on local compute without a GPU. |
RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings (2024.emnlp-industry)
Copied to clipboard
| Challenge: | Existing studies constrain questions and answers within predefined contexts, excluding the retrieval process. |
| Approach: | They present a retrieval-augmented conversation dataset that addresses key challenges . they propose a system that combines query rewriting and retrieval with reranking . |
| Outcome: | The proposed system improves query rewriting, retrieval, reranking, and response generation performance. |
A Compare Aggregate Transformer for Understanding Document-grounded Dialogue (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have focused on KS in unstructured documents, but dialogue history that is not related to the current dialogue may introduce noise in the KS processing. |
| Approach: | They propose a Compare Aggregate Transformer to jointly denoise the dialogue context and aggregate the document information for response generation. |
| Outcome: | The proposed model outperforms the state-of-the-art approach and strong baselines on a CMU_DoG dataset. |
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)
Copied to clipboard
| Challenge: | Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory . |
| Approach: | They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response . |
| Outcome: | The proposed approach significantly improves the informativeness of the generated responses. |
Data-Efficient Automatic Prompt Optimization for Memory-Enhanced Conversational Agents (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Automatic prompt optimization (APO) uses algorithms to optimize prompts for LLMs . but application to memory-enhanced conversational agents presents unique challenges . |
| Approach: | They propose a framework for automatic prompt optimization for memory-enhanced conversational agents . they leverage LLMs to holistically optimize the prompts of all agents based on memory writing, reading, and response generation . |
| Outcome: | The proposed framework is applied to memory-enhanced conversational agents . it provides a holistic quality score for responses and performs error attribution . |
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions. |
| Approach: | They develop local and global explanation methods and a feed-forward-based method for input-output attribution to investigate the impact of instruction tuning on user intentions. |
| Outcome: | The proposed method compares explanations from pre-trained and instruction-tuned models . it empowers LLMs to recognize the instruction parts of user prompts, it encourages response generation . |
Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing systems for task oriented dialog use knowledge present only in structured knowledge sources to generate responses. |
| Approach: | They propose a model that assumes that information is always present in a structured knowledge base . they also refine the model to take into account the fact that it can fuse information from structured and unstructured knowledge sources. |
| Outcome: | The proposed model is robust to perturbations to knowledge modality and can fuse information from structured and unstructured knowledge to generate responses. |
Tailored Sequence to Sequence Models to Different Conversation Scenarios (P18-1)
Copied to clipboard
| Challenge: | Sequence to sequence (Seq2Sequeq) models fail to meet the diverse requirements for different conversation scenarios, such as customer service and chatbot. |
| Approach: | They propose two optimized criteria for Sequence to sequence (Seq2Sequeq) to meet different conversation scenarios, i.e., maximum generated likelihood for specific-requirement scenario, and conditional value-at-risk for diverse-requrement scenarios. |
| Outcome: | The proposed models satisfies diverse requirements for different conversation scenarios and yields better performances than existing models. |
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge grounding models focus on locating knowledge in document contexts that are relevant to the conversation. |
| Approach: | They propose a knowledge identification model that leverages document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. |
| Outcome: | The proposed model can be applied to document-grounded conversational datasets and shows generalization to unseen documents and long dialogue contexts. |
Investigating Content Planning for Navigating Trade-offs in Knowledge-Grounded Dialogue (2024.eacl-long)
Copied to clipboard
| Challenge: | Knowledge-grounded dialogues require a balance between being specific to what the conversation partner has said and being attributable to an underlying source document. |
| Approach: | They propose a framework that allows to experiment with various plan variables supported by prior work . they show that metric-aware planning mechanisms are better at automatic evaluations but underperform in human judgment compared to metric agnostic mechanisms. |
| Outcome: | The proposed framework supports metric-agnostic and metric aware content planning, but it underperforms in human judgment. |
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsensense inferences during response generation. |
| Approach: | They propose to separate commonsense reasoning into explicit steps for generating, selecting, and integrating commonsensense into dialogue responses. |
| Outcome: | The proposed model infers commonsense knowledge from dialogue contexts to improve response quality and naturalness of dialogue interactions. |
Generative Knowledge Selection for Knowledge-Grounded Dialogues (2023.findings-eacl)
Copied to clipboard
| Challenge: | Knowledge selection is the key in knowledge-grounded dialogues (KGD), which aims to select an appropriate knowledge snippet to be used in the utterance based on dialogue history. |
| Approach: | They propose a generative approach for knowledge selection called GenKS that learns to select snippets by generating their identifiers with a sequence-to-sequence model. |
| Outcome: | The proposed approach captures intra-knowledge interaction inherently through attention mechanisms while generating their identifiers with a sequence-to-sequence model. |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing reasoning large language models (LLMs) generate responses without explicitly aligning thoughts with counseling techniques, limiting their effectiveness. |
| Approach: | They propose a lightweight thinking model that generates therapeutic thoughts to guide MI counseling agents in strategy selection and response generation. |
| Outcome: | The proposed model achieves theory-of-mind assessment comparable to state-of the-art systems with an order of magnitude less computation. |
Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing word-level policy models that learn dialog policy and language generation from dialog corpora often lead to degeneration issues where the utterances become ungrammatical or repetitive. |
| Approach: | They propose to represent prior dialog transitions as a graph and learn a CG grounded dialog policy that can foster a more coherent and controllable dialog. |
| Outcome: | The proposed framework is able to learn dialog policy in open-domain multi-turn conversation. |
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes (2021.emnlp-main)
Copied to clipboard
| Challenge: | Empathy is a complex cognitive ability based on the reasoning of others’ affective states. |
| Approach: | They propose a method to infer emotion cause words from utterances without a word-level label and a novel method to make dialogue models focus on targeted words in the input during generation. |
| Outcome: | The proposed method improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of automatic and human evaluation. |
CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to knowledge-grounded dialogue generation perform relatively independent sub-tasks . Typical approaches tend to decompose this task into two streamlined sub- tasks . |
| Approach: | They propose a collaborative latent variable model to integrate knowledge selection and knowledge-aware response generation simultaneously in separate but collaborative latences. |
| Outcome: | The proposed model outperforms previous methods on knowledge selection and response generation. |
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism (2023.acl-long)
Copied to clipboard
| Challenge: | Existing generative models for dialogue use the last hidden state to summarize the history of the dialogue. |
| Approach: | They propose a Pseudo-Variational Gated Recurrent Unit (PVGRU) that summarises the accumulated distribution variations of subsequences and builds a model based on it. |
| Outcome: | The proposed model can improve diversity and relevance of responses on two benchmark datasets. |
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs (D19-1)
Copied to clipboard
| Challenge: | Existing knowledge-based open domain conversation generation models are limited by the use of unstructured knowledge texts. |
| Approach: | They propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. |
| Outcome: | The proposed system is more explainable and flexible than state-of-the-art models. |
Unsupervised Context Rewriting for Open Domain Conversation (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to model conversation context have drawbacks, such as lack of coreferences and long dependency. |
| Approach: | They propose a context rewriting method which explicitly rewrites the last utterance by considering context history. |
| Outcome: | The proposed method outperforms baselines in terms of rewriting quality, multi-turn response generation, and end-to-end retrieval-based chatbots. |
Low-Resource Response Generation with Template Prior (D19-1)
Copied to clipboard
| Challenge: | Existing open domain response generation models are limited to paired data, but are less explored in real-world applications. |
| Approach: | They propose to train a neural response generation model with unpaired data and paired data as prior. |
| Outcome: | The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available. |
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)
Copied to clipboard
| Challenge: | Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation. |
| Approach: | They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large-scale real-world dataset. |
Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for self-training from meaning representations (MRs) are noisy or uninformative for the model to learn from. |
| Approach: | They propose a two-phase procedure to generate high-quality pseudo-labeled MR-to-Text pairs by aggregating multiple perturbed latent representations from each MR. |
| Outcome: | Empirical results on two benchmark datasets show that the proposed procedure outperforms existing methods on automatic and human evaluations. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
Copied to clipboard
Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |
Mitigating Contradictions in Dialogue Based on Contrastive Learning (2022.findings-acl)
Copied to clipboard
| Challenge: | Current chatbots generate fluent, informative responses but sometimes generate contradictory responses when interacting with human. |
| Approach: | They propose to use contrastive learning technique to mitigate contradiction issues in chatbots by minimizing the similarity between the target response and contradiction related negative example. |
| Outcome: | The proposed method outperforms existing methods on automatic and human evaluation while preserving response fluency. |
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning (2021.findings-acl)
Copied to clipboard
| Challenge: | PLATO-2 is a high-quality open-domain chatbot that can generate one-to-many mappings and improve response quality. |
| Approach: | They propose a curriculum learning process to build a high-quality open-domain chatbot . they use a coarse-grained generation model and latent variables to train a generative model . |
| Outcome: | The proposed model improves on Chinese and English data and can generate diverse responses and select the best response. |
NewsDialogues: Towards Proactive News Grounded Conversation (2023.findings-acl)
Copied to clipboard
Siheng Li, Yichun Yin, Cheng Yang, Wangjie Jiang, Yiwei Li, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang
| Challenge: | Hot news is one of the most popular topics in daily conversations. |
| Approach: | They propose a task where a dialogue system can lead the conversation based on key topics of the news. |
| Outcome: | The proposed method can lead conversations based on key topics of the news . it can also be used in information-seeking and chit-chat scenarios . |
Contextual Dynamic Prompting for Response Generation in Task-oriented Dialog Systems (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing studies show that large pre-trained language models can be adapted to task-oriented dialog systems. |
| Approach: | They propose to use contextual dynamic prompting to generate prompts in dialogs . they propose to distill useful prompting signals from dialog contexts based on contextual dynamic . |
| Outcome: | The proposed approach improves response generation by 3 points and 17 points when dialog states are incorporated. |
Think Beyond Words: Exploring Context-Relevant Visual Commonsense for Diverse Dialogue Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to generate intelligent open-domain dialogue agents only consider auxiliary commonsense stored in pure text, ignoring grounding information from the external visual world. |
| Approach: | They propose a VIsual Commonsense enhanced dialogue generaTOR that exploits auxiliary commonsense from images related to context to generate coherent and informative responses. |
| Outcome: | The proposed method outperforms the latest competitive methods in terms of coherence and diversity on two public datasets. |
From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing research on empathy generation focuses on understanding the emotions of the speaker rather than on how the responder conveys empathy. |
| Approach: | They propose to use figurative language and causal semantic context to facilitate targeted empathy generation in a mental health support domain. |
| Outcome: | The proposed approach achieves 7.6% improvement in BLEU, 36.7% reduction in Perplexity, and 7.6% increase in lexical diversity. |
MultiAgentESC: A LLM-based Multi-Agent Collaboration Framework for Emotional Support Conversation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies focus on generating responses directly and neglect integration of domain-specific reasoning and expert interaction. |
| Approach: | They propose a training-free multi-agent collaboration framework for ESC to emulate human-like process of providing emotional support through dialogue analysis, strategy deliberation, and response generation. |
| Outcome: | The proposed framework excels at providing emotional support and diversifying support strategy selection. |
Controlling Dialogue Generation with Semantic Exemplars (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to control dialogue generation are manual labelling and manual editing of data. |
| Approach: | They propose a method to control dialogue generation using exemplar responses . they use semantic frames present in exemplars to guide response generation . |
| Outcome: | The proposed model improves coherence while preserving semantic meaning and conversation goals . exemplar responses are handwritten or strategically curated to promote highlevel goals without explicit labels . |
EMA: An Episodic Memory Agent for Efficient and Selective Memory (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing memory-augmented methods often incorporate full dialog histories without filtering, resulting in information redundancy and inference latency. |
| Approach: | They propose a framework that abstracts conversational context into Episodic Memory Units (EMUs) they propose EMA, MemDecider and a filtering decision module to reduce noise and improve overall performance. |
| Outcome: | The proposed framework reduces token consumption by 11.48% while improving performance on two widely-used benchmarks. |
Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing approaches to improve the quality of persona-grounded dialogues are limited to a few informative words. |
| Approach: | They propose a concept-based persona expansion framework that takes the original persona as input and generates expanded personas that contain conceptually rich content. |
| Outcome: | The proposed framework improves the quality of persona-grounded dialogue responses in diversity and richness. |
RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models struggle to evaluate the correctness of non-parametric knowledge when it differs from internal memorization, leading to knowledge conflicts during response generation. |
| Approach: | They propose a lightweight alignment method to leverage multi-source knowledge based on retrieval relevance. |
| Outcome: | Experiments on four datasets show that the proposed method outperforms RAG by 4-10% in accuracy without any extra component. |
External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems (2023.eacl-main)
Copied to clipboard
| Challenge: | End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired during training. |
| Approach: | They propose an architecture for document-oriented conversations with access to external knowledge sources. |
| Outcome: | The proposed architecture outperforms baseline models on the Wizard of Wikipedia dataset by 10.3% and 7.4%. |
Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to detect toxic generation of pretrained language models rely on templates, data extraction, crowdsourcing workers or automatic generation. |
| Approach: | They propose a method to construct adversarial contexts conditioned on a given response . they augment existing dataset BAD+ and construct a new dataset B AD+ . |
| Outcome: | The proposed method can detect toxic or biased content in large pretrained language models. |
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation (2025.findings-acl)
Copied to clipboard
Xueru Wen, Jie Lou, Xinyu Lu, Yuqiu Ji, Xinyan Guan, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. |
| Approach: | They propose a framework that allows large language models to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
| Outcome: | The proposed framework enables LLMs to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing dialogue systems focus on brief single-session interactions, neglecting real-world needs for long-term companionship and personalized interactions. |
| Approach: | They propose a model-agnostic framework for long-term dialogue agents . they use event summary and persona management to enable reasoning . |
| Outcome: | The proposed framework incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model . |
| Approach: | They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in response quality and human judgment . it also enjoys a faster decoding process . |
Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges (2025.findings-acl)
Copied to clipboard
Hongru Wang, Wenyu Huang, Yufei Wang, Yuanhao Xi, Jianqiao Lu, Huan Zhang, Nan Hu, Zeming Liu, Jeff Z. Pan, Kam-Fai Wong
| Challenge: | Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use focus on stateless, single-turn interactions or partial evaluations, overlooking the inherent stateful nature of interactions in multi-turn applications. |
| Approach: | They propose a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use across six key tasks in three stages . they also build VirtualMobile – an embodied virtual mobile evaluation environment to simulate API calls and assess the robustness of the created APIs. |
| Outcome: | The proposed dataset evaluates 13 open- and closed-source LLMs and provides detailed analysis at each stage. |
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing variational dialog models have pre-trained, restricting diversity of responses . a diffusion model increases complexity of prior distribution and its compatibility with PLMs . |
| Approach: | They propose a hierarchical conditional variational autoencoder with diffusion priors to address these challenges. |
| Outcome: | The proposed method generates more diverse responses without dialog pre-training. |
STRIDE-ED: A Strategy-Grounded Stepwise Reasoning Framework for Empathetic Dialogue Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Empathetic dialogue requires not only recognizing a user’s emotional state but also making strategy-aware, context-sensitive decisions throughout response generation. |
| Approach: | They propose a STRategy-grounded, interpretable, and DEep reasoning framework that models Empathetic Dialogue through structured, strategy-conditioned reasoning. |
| Outcome: | The proposed framework outperforms existing methods on automatic metrics and human evaluations. |
LSTDial: Enhancing Dialogue Generation via Long- and Short-Term Measurement Feedback (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing dialogue systems do not utilize quality dimensions specifically designed for dialogue evaluation to guide the response generation during training. |
| Approach: | They propose a two-stage framework which generates and utilizes conversation evaluation as explicit feedback during training. |
| Outcome: | The proposed framework generates and utilizes conversation evaluation as explicit feedback during training. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
Copied to clipboard
Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (2023.emnlp-main)
Copied to clipboard
Hyungjoo Chae, Yongho Song, Kai Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo
| Challenge: | a human-like chatbot requires commonsense reasoning to comprehend and respond to information . however, identifying and aggregating key evidence within a single hop is a challenge . a knowledge distillation framework is proposed that leverages LLMs as unreliable teachers . |
| Approach: | They propose a framework that leverages large language models as unreliable teachers to facilitate multi-hop reasoning over a dialogue context. |
| Outcome: | The proposed framework leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
Copied to clipboard
Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
Probing Commonsense Explanation in Dialogue Response Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Currently, response generation (RG) models do not understand human communication intents. |
| Approach: | They propose to examine commonsense reasoning implicitly to determine whether RG models produce coherent responses in conversations. |
| Outcome: | The proposed probing settings show that RG models fail to capture the logical relations between commonsense explanations and responses and fine-tuning on in-domain data do not lead to understanding of CSR for RG. |
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations. |
| Approach: | They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel. |
Contextualize Knowledge Bases with Transformer for End-to-end Task-Oriented Dialogue Systems (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to integrate knowledge bases into end-to-end task-oriented dialogue systems are limited in their ability to properly represent the entity of KB. |
| Approach: | They propose a framework that dynamically perceives all relevant entities and dialogue history . it uses a Memory Mask to enforce the entity to focus on its relevant entities . |
| Outcome: | The proposed framework can achieve superior performance over the state of the arts. |
Variational Autoregressive Decoder for Neural Response Generation (D18-1)
Copied to clipboard
| Challenge: | Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses. |
| Approach: | They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence. |
| Outcome: | Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets. |
Jailbreaks as Inference-Time Alignment: A Framework for Understanding Safety Failures in LLMs (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models are safety-aligned to prevent harmful response generation . prior work on jailbreak effectiveness has focused on analyzing success rate of jailbreaks . |
| Approach: | They propose to frame jailbreaks as inference-time alignment and draw suboptimal bounds . they also propose a Safety-Net to measure how vulnerable an LLM is to jailbreak attacks . |
| Outcome: | a new framework allows researchers to show how vulnerable an LLM is to jailbreaks . a Safety-Net measures how vulnerable the model is to attacks, the authors say . |
Towards Topic-Guided Conversational Recommender System (2020.coling-main)
Copied to clipboard
| Challenge: | Existing CRS datasets focus on immediate requests from users, while lack proactive guidance to the recommendation scenario. |
| Approach: | They propose a topic-guided conversational recommendation dataset . it incorporates topic threads to enforce natural semantic transitions towards the recommendation scenario . |
| Outcome: | The proposed approach is more reasonable and controllable than previous approaches. |
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation (P19-1)
Copied to clipboard
| Challenge: | Conventional methods employ a fixed vocabulary and one-pass decoding, which make them prone to safe and general responses and lack further refinement to the first generated raw sequence. |
| Approach: | They propose a Vocabulary Pyramid Network which integrates multi-pass encoding and decoding with multi-level vocabularies into response generation. |
| Outcome: | The proposed system outperforms strong baselines on English Twitter and Chinese Weibo datasets. |
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration (2023.acl-long)
Copied to clipboard
Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Meeyoung Cha, Yejin Choi, Byoungpil Kim, Gunhee Kim, Eun-Ju Lee, Yong Lim, Alice Oh, Sangchul Park, Jung-Woo Ha
| Challenge: | Existing studies focus on coping with social harms that large language models pose . however, discussions on sensitive issues can become toxic even if the users are well-intentioned. |
| Approach: | They propose to use Korean dataset to test whether LLMs can generate offensive content and propagate prejudices. |
| Outcome: | The proposed dataset shows that acceptable response generation improves for HyperCLOVA and GPT-3. |
Learning to Abstract for Memory-augmented Conversational Response Generation (P19-1)
Copied to clipboard
| Challenge: | Existing generative models for open-domain chit-chat conversations lack informativeness and diversity. |
| Approach: | They propose a retrieval-augmented generative model that learns to abstract from the training corpus and saves useful information to the memory to assist the response generation. |
| Outcome: | The proposed model outperforms other baselines in query-response clustering and learning to utilize these characteristics for response generation. |
Modulating Language Models with Emotions (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for generating context-aware language that embodies diverse emotions are dull or generic due to limited training data for diverse emotions. |
| Approach: | They propose a modulated layer normalization technique that generates emotional responses using large pre-trained models. |
| Outcome: | The proposed method outperforms baseline methods on the MojiTalk dataset while maintaining diversity, fluency, and coherence. |
Compressing Context to Enhance Inference Efficiency of Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated remarkable power and impressive generalisation abilities across various tasks. |
| Approach: | They propose a method that prunes redundancies in the input context to make the input more compact. |
| Outcome: | The proposed method reduces memory and inference time while maintaining comparable performance compared to full context. |
Elastic Weight Removal for Faithful and Abstractive Dialogue Generation (2024.naacl-long)
Copied to clipboard
| Challenge: | Current-day large language models generate coherent, grammatical, and seemingly meaningful text, but are prone to hallucinating incorrect information. |
| Approach: | They propose to ‘subtract’ parameters of a model trained to hallucinate from a dialogue response generation model to ‘negate’ the contribution of such hallucinatedexamples from it. |
| Outcome: | The proposed method reduces hallucinations and discourages extractive responses, which are often a consequence of reducing hallucines by encouraging copy-pasting of document spans. |
An Investigation of Suitability of Pre-Trained Language Models for Dialogue Generation – Avoiding Discrepancies (2021.findings-acl)
Copied to clipboard
| Challenge: | Pre-trained language models have been widely used in open-domain dialogue generation. |
| Approach: | They propose to use decoder-only architecture to achieve excellent performance for dialogue generation. |
| Outcome: | The proposed frameworks are based on transformer-ED, transformer-Dec, transformer MLM and transformer-AR. |
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems (2023.emnlp-main)
Copied to clipboard
| Challenge: | Current approaches to task-oriented dialogue systems integrate knowledge retrieval and response generation, which poses scalability challenges when dealing with extensive knowledge bases. |
| Approach: | They propose a retriever-generator architecture that harnesses a retrieval and a generator to generate system responses by using feedback from the generator as pseudo-labels. |
| Outcome: | The proposed architecture shows superior performance on three benchmark datasets. |
Language Model Detoxification in Dialogue with Contextualized Stance Control (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on Language Model detoxification has focused on reducing the toxicity of the generation itself without consideration of the context. |
| Approach: | They propose a method to do context-dependent detoxification without taking into account the stance of the generated response. |
| Outcome: | The proposed method can learn the context-dependent stance control strategies while keeping a low self-toxicity of the underlying LM. |
Speculative Sampling in Variational Autoencoders for Dialogue Response Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have tried to improve variational models but they fail to learn proper mappings. |
| Approach: | They propose to use a variable-based sampling technique to find the most probable one from redundantly sampled latent variables to tie up the variable with a given response. |
| Outcome: | The proposed method is effective in response generation with massive dialogue data constructed from Twitter posts. |
Contextual Knowledge Learning for Dialogue Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. |
| Approach: | They propose a method to incorporate conversational context and knowledge into dialogue generation models . they use Latent Vectors to capture the relationship between context and knowing . |
| Outcome: | The proposed approach improves performance with two standard datasets and human evaluations. |
Towards Lifelong Dialogue Agents via Timeline-based Memory Management (2025.naacl-long)
Copied to clipboard
Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak, Hyungjoo Chae, Taeyoon Kwon, Yohan Jo, Seung-won Hwang, Dongha Lee, Jinyoung Yeo
| Challenge: | Existing studies focus on getting rid of outdated memories to improve retrieval quality, but we argue that such memories provide rich, important contextual cues for response generation (RG). |
| Approach: | They propose a framework for LLM-based lifelong dialogue agents that discards memory removal and manages large-scale memories by linking them based on their temporal and cause-effect relation. |
| Outcome: | The proposed framework augments RG with memory timelines based on evolution or causality of relevant past events. |
MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space (2024.emnlp-main)
Copied to clipboard
Yihong Tang, Bo Wang, Dongming Zhao, Jinxiaojia Jinxiaojia, Zhangjijun Zhangjijun, Ruifang He, Yuexian Hou
| Challenge: | Personalized Dialogue Generation relies on external role data, which can be scarce and raise privacy concerns. |
| Approach: | They propose a framework to extract role information from dialogue history . they use persona codebook to represent roles in latent space and posterior distribution of role information . |
| Outcome: | The proposed framework can generalize across roles, even for unseen roles. |
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for dialog agent training lack a robust action space for entangled information, which can cause bias and deviate from natural human language. |
| Approach: | They propose phrase-level action reinforcement learning which allows the model to alter the sentence structure and content with the sequential action selection. |
| Outcome: | The proposed model achieves competitive results with state-of-the-art models on the MultiWOZ dataset, indicating that it is effective for solving task-oriented dialogs. |
Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for learning task-oriented dialog systems filter irrelevant KB information over a large KB. |
| Approach: | They propose a pairwise similarity filter that respects the n-ary structure in a KB record and an auxiliary loss that helps in separating contextually unrelated KB information. |
| Outcome: | The proposed method outperforms existing state-of-the-art models on three publicly available datasets. |
Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on developing persona consistent dialogue models . order sensitivity affects the quality and consistency of generated response . |
| Approach: | They propose a model-agnostic framework to improve persona consistent dialogue response generation by concatenating persona texts and dialogue history as a single input sequence. |
| Outcome: | The proposed framework outperforms existing models on the Persona-Chat dataset and shows that it is more robust under different persona orders and more consistent with the persona profile. |
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing RL methods focus on generation tasks while neglecting dialogue state tracking (DST) for understanding. |
| Approach: | They propose a method that integrates RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation. |
| Outcome: | The proposed approach achieves state-of-the-art results on three widely used datasets. |
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented Dialogues (2024.lrec-main)
Copied to clipboard
| Challenge: | User Simulators are used to train task-oriented dialogue systems . traditional training paradigms rely on human-engineered agendas resulting in generated responses that lack diversity and spontaneity. |
| Approach: | They propose a framework that leverages large language models to generate diverse responses . they use two LLMs to generate and verify responses, which are preferred by users . |
| Outcome: | The proposed framework produces responses that exhibit diversity and are preferred by human users. |
Q-TOD: A Query-driven Task-oriented Dialogue System (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing pipelined task-oriented dialogue systems have difficulties adapting to unseen domains . end-to-end systems are plagued by large-scale knowledge bases in practice . |
| Approach: | They propose a query-driven task-oriented dialogue system that extracts dialogue context information into a natural language query. |
| Outcome: | The proposed system outperforms strong baselines and establishes a new state-of-the-art performance on three publicly available task-oriented dialogue datasets. |
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System (2023.emnlp-main)
Copied to clipboard
| Challenge: | generative models struggle to distinguish subtle differences among retrieved knowledge records, resulting in suboptimal quality of generated responses. |
| Approach: | They propose to use maximum marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision. |
| Outcome: | The proposed approach improves on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models. |
Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to knowledge retrieval are limited by the knowledge base encoder, but our work focuses on the knowledge-base encoder. |
| Approach: | They propose an approach that utilizes topic modeling on the knowledge base to improve retrieval accuracy and as a result, improve response generation. |
| Outcome: | The proposed approach can improve retrieval and generation performance on two datasets. |
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state- of-the art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial. |
| Approach: | They propose to integrate topical information into knowledge-grounded task-oriented dialogue systems by using multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources. |
| Outcome: | The proposed model outperforms existing models in knowledge selection and response generation. |
Neural Response Generation with Meta-words (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP . |
| Approach: | They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller. |
| Outcome: | The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy. |
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling (D18-1)
Copied to clipboard
Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gašić
| Challenge: | a dataset of 10k human-human written conversations is one order of magnitude larger than previous annotated task-oriented corpora. |
| Approach: | They propose to collect 10k human-human written conversations from a crowd-sourced dataset using crowd-sourcing. |
| Outcome: | The proposed dataset is one order of magnitude larger than previous annotated task-oriented corpora and shows the usability of the data and sets a baseline for future studies. |
A Dataset of General-Purpose Rebuttal (D19-1)
Copied to clipboard
Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | a key element in argumentation is rebuttal, the ability to contest an argument by presenting a counter-argument. |
| Approach: | They propose a method based on general rebuttal arguments to produce a critical response to a long argumentative text. |
| Outcome: | The proposed method overcomes the need for topic-specific arguments to be provided . it allows creating responses beyond the scope of topics for which specific arguments are available . |
Improving Neural Conversational Models with Entropy-Based Data Filtering (P19-1)
Copied to clipboard
| Challenge: | Current neural network-based conversational models lack diversity and generate boring responses to open-ended utterances. |
| Approach: | They propose an unsupervised method of filtering dialog datasets by removing generic utterances from training data using an entropy-based approach that does not require human supervision. |
| Outcome: | The proposed method improves dialog quality as chatbots learn to output more diverse responses to open-ended utterances. |
LBC: Language-Based-Classifier for Out-Of-Variable Generalization (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have great success in natural language processing tasks such as response generation, but their performance on tabular data tasks has been limited due to their inferior performance compared to traditional machine learning models (TMLs). |
| Approach: | They propose a Language-Based-Classifier (LBC) that maximizes the benefits of LLMs to outperform TMLs on OOV tasks. |
| Outcome: | The proposed model outperforms TMLs on OOV tasks by using three key methods. |
Separate the Wheat from the Chaff: Winnowing Down Divergent Views in Retrieval Augmented Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) lack robustness in knowledge-intensive tasks due to noisy or irrelevant retrieved data. |
| Approach: | They propose a multi-agent debate-based RAG framework that integrates external knowledge sources into large language models to improve their accuracy. |
| Outcome: | The proposed framework is unsupervised and leverages pretrained LLMs without fine-tuning, making it easily adaptable to various tasks. |
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Auto-SLURP is a benchmark dataset for evaluating multi-agent frameworks powered by large language models. |
| Approach: | Auto-SLURP is a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks . authors propose it extends original SLURP dataset by relabeling data and integrating simulated servers and external services. |
| Outcome: | The proposed dataset extends the original SLURP dataset for natural language understanding tasks. |
Inside Out: Evolving User-Centric Core Memory Trees for Long-Term Personalized Dialogue Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Existing personalized dialogue systems struggle to reconcile unbounded interactions with finite context constraints. |
| Approach: | They propose a framework that utilizes a globally maintained PersonaTree as the carrier of long-term user profiling. |
| Outcome: | The proposed framework outperforms existing systems in suppressing contextual noise and persona inconsistency. |
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog (2023.acl-long)
Copied to clipboard
| Challenge: | Existing systems blend knowledge retrieval with response generation and optimize them with direct supervision from reference responses. |
| Approach: | They propose a multi-grained knowledge retrieval system that decouples knowledge retrievals from response generation and introduces an entity selector and an attribute selector to acquire multigrained information from the knowledge base. |
| Outcome: | The proposed system performs better on small and large knowledge bases. |
Neuro-Symbolic Query Compiler (2025.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) systems are limited in their ability to process information in open-source environments. |
| Approach: | They propose a neuro-symbolic framework inspired by linguistic grammar rules and compiler design to formalize complex queries using a minimal yet sufficient Backus-Naur Form grammar. |
| Outcome: | The proposed framework is based on a backus-naur form grammar and compiler design that maintains completeness while minimizing redundancy. |
Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to rewrite ambiguous queries ignore feedback from query rewriting, passage retrieval and response generation in the rewritten process. |
| Approach: | They propose to construct self-consistent preference alignment data to generate more diverse rewritten queries. |
| Outcome: | The proposed method is effective in both in- and out-of-distribution scenarios. |
Multi-Domain Dialogue Acts and Response Co-Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation. |
| Approach: | They propose a neural co-generation model that generates dialogue acts and responses concurrently and preserves semantic structures of multi-domain dialogue acts. |
| Outcome: | The proposed model improves over state-of-the-art models in automatic and human evaluations on a large-scale dataset. |
AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders (2025.findings-emnlp)
Copied to clipboard
| Challenge: | AMIA is a lightweight, inference-only defense for Large Vision–Language Models . it automatically masks text-irrelevant image patches and conducts joint Intention Analysis . |
| Approach: | AMIA is a lightweight, inference-only defense for large vision–language models . it automatically masks a small set of text-irrelevant image patches to disrupt adversarial perturbations . |
| Outcome: | AMIA improves defense success rates across diverse LVLMs and jailbreak benchmarks . it preserves general utility with only 2% accuracy drop, incurs only modest inference overhead . |
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities. |
| Approach: | They propose a framework that integrates emotion correlation learning, utilization, and supervising. |
| Outcome: | The proposed framework improves empathetic perception and expression on a humanized dialogue dataset. |
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have shown that ChatGPT has limitations such as failing to ask clarifying questions to ambiguous queries or refusing problematic user requests. |
| Approach: | They propose a Proactive Chain-of-Thought prompting scheme which augments LLMs with the goal planning capability over descriptive reasoning chains to trigger proactivity. |
| Outcome: | The proposed scheme augments LLMs with the goal planning capability over descriptive reasoning chains to trigger the proactivity of LLM-based proactive dialogue systems. |
Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality (2022.emnlp-main)
Copied to clipboard
| Challenge: | Currently, human communication models fail to explicitly model common ground (CG) . less than half of the responses in current data is rated as high quality . |
| Approach: | They propose a dataset that annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |
| Outcome: | The proposed dataset annotates dialogues with explicit CG and solicits 9k diverse responses each following one common ground. |
Disfluency Generation for More Robust Dialogue Systems (2023.findings-acl)
Copied to clipboard
| Challenge: | Disfluencies in user utterances can trigger a chain of errors impacting all the modules of a dialogue system. |
| Approach: | They propose to augment existing dialogue datasets with disfluent utterances by paraphrasing them into disfluente ones. |
| Outcome: | The proposed method improves dialogue state tracking and response generation by combining disfluent utterances with disfluency utteraces. |
LLM-Enhanced Query Generation and Retrieval Preservation for Task-Oriented Dialogue (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge retrieval methods for task-oriented dialogues are limited by data scarcity and lack of data to annotate. |
| Approach: | They propose an LLM-enhanced model of query-guided knowledge retrieval for task-oriented dialogue . they propose to select the most relevant knowledge from retrieved top-K records and incorporate them as prompts to guide a generator in response generation. |
| Outcome: | The proposed model outperforms state-of-the-art in three benchmarks on three standard benchmarks. |
Structural Constraints and Natural Language Inference for End-to-End Flowchart Grounded Dialog Response Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to learn flowchart grounded dialogs have two limitations . Flowchart-based systems require only the chat transcripts and no additional annotations . |
| Approach: | They propose a structure-aware approach to learn flowchart grounded dialogs . it uses structural constraints derived from connectivity structure of flowchartes into a RAG framework . |
| Outcome: | The proposed approach outperforms existing approaches with a success rate of 68% and 123%. |
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment (2023.acl-long)
Copied to clipboard
| Challenge: | Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning . MU can be used to forget specific training instances as if they have never existed . |
| Approach: | They propose a general unlearning framework called KGA to induce forgetfulness . they propose several unlearning evaluation metrics with pertinence . |
| Outcome: | The proposed framework improves on large-scale datasets and provides insight into unlearning for NLP tasks. |
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on social biases in language models have focused on only English. |
| Approach: | They propose to use a Chinese dataset for bias evaluation and mitigation of Chinese conversational language models. |
| Outcome: | The proposed dataset includes under-explored bias categories, such as ageism and appearance biases, which received less attention in previous studies. |
Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models focus on identifying specific types of dialogue knowledge and utilizing corresponding datasets for training, but lack generalization capabilities and computational resources. |
| Approach: | They propose a framework that explores multi-source multi-type knowledge from LLMs by leveraging diverse datasets and exploits it for response generation. |
| Outcome: | The proposed framework exploits multi-source multi-type knowledge from LLMs to generate coherent, informative, and fluent responses. |
Response-conditioned Turn-taking Prediction (2023.findings-acl)
Copied to clipboard
| Challenge: | Traditionally, turn-taking is done using a simple silence threshold, but more modern approaches use cues known to be important in human-human turn-shifts. |
| Approach: | They propose a turn-taking and response-ranking model that conditions the end-of-turn prediction on conversation history and what the next speaker wants to say. |
| Outcome: | The proposed model outperforms the baseline model in a variety of metrics. |
Improving the Robustness of Large Language Models via Consistency Alignment (2024.lrec-main)
Copied to clipboard
Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin
| Challenge: | Large language models have shown tremendous success in following user instructions and generating helpful responses, but their robustness is still far from optimal. |
| Approach: | They propose a two-stage training framework that helps a model generalize on following instructions via similar instruction augmentations. |
| Outcome: | The proposed training framework improves diversity and aligns the model with human expectations by differentiating subtle differences in similar responses. |
Focus-aware Response Generation in Inquiry Conversation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on response generation focus on relevance and fluency, rarely paying attention to the focus. |
| Approach: | They propose a Focus-aware response generation method that takes the focus into consideration and optimizes a multi-level encoder and focal decoder to generate multiple candidate responses. |
| Outcome: | The proposed method generates candidate responses that correspond to different focuses and performs better on two orthogonal inquiry conversation datasets. |
I Remember You!: SUI Corpus for Remembering and Utilizing Users’ Information in Chat-oriented Dialogue Systems (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for remembering and utilizing information on users in system utterances do not always fit the context of the dialogue. |
| Approach: | They propose to use user information to fill in utterance templates but the utterrances do not always fit the context. |
| Outcome: | The proposed system can remember and utilize user information on users in dialogues while keeping appropriateness for the context. |
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Standard RALMs often neglect their intrinsic knowledge due to the interference from retrieved information. |
| Approach: | They propose a new approach to improve robustness of RALMs by generating sequential reading notes for each retrieved document. |
| Outcome: | The proposed approach outperforms standard RALMs on four open-domain QA benchmarks. |
TORSO: Template-Oriented Reasoning Towards General Tasks (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate responses using few-shot examples depend on the provided examples, limiting the model’s reasoning capabilities. |
| Approach: | They propose a model that emulates human reasoning during response generation by using curated few-shot prompts instead of manually crafted few-shot examples. |
| Outcome: | The proposed model achieves strong performance on diverse LLMs benchmarks with reasonable rationales. |
Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing research on multi-party dialogue generation has focused on structural information inherent in dialogues, but colloquial expressions and incomplete utterances often impede comprehension and weaken the fidelity of dialogue structure representations. |
| Approach: | They propose a framework to improve multi-party dialogue generation through dialogue context rewriting using two complementary feedback signals to construct preference data for both context & response generation. |
| Outcome: | The proposed framework improves multi-party dialogue generation through dialogue context rewriting. |
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2023.findings-emnlp)
Copied to clipboard
Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur
| Challenge: | Dialogue models are able to generate fluent and interesting responses, but they can be difficult to control and may produce non-engaging, unsafe results. |
| Approach: | They propose a framework for controlling dialogue model behavior using natural language rules, or guidelines, which provide information about the context they are applicable to and what should be included in the response. |
| Outcome: | The proposed framework is effective in three open-domain dialogue response generation tasks and is consistent with the developer's expectations and intent. |
FacLens: Transferable Probe for Foreseeing Non-Factuality in Fact-Seeking Question Answering of Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing non-factuality detection methods require response generation, which incurs significant computational overhead. |
| Approach: | They propose a lightweight model called Factuality Lens which effectively probes hidden representations of fact-seeking questions for the NFP task. |
| Outcome: | The proposed model is able to probe hidden representations of fact-seeking questions and reduce development costs. |
Please note that I’m just an AI: Analysis of Behavior Patterns of LLMs in (Non-)offensive Speech Identification (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming a part of our everyday lives by being used as tools for information search, content creation, writing assistance, and many more. |
| Approach: | They propose to use Large Language Models to detect offensive online language in applications with social risk, such as late-life companions and online content moderators. |
| Outcome: | The proposed models fail to detect offensive language and are therefore unsuitable for use in social applications such as late-life companions and online content moderators. |
Flexibly Utilize Memory for Long-Term Conversation via a Fragment-then-Compose Framework (2025.emnlp-main)
Copied to clipboard
Cai Ke, Yiming Du, Bin Liang, Yifan Xiang, Lin Gui, Zhongyang Li, Baojun Wang, Yue Yu, Hui Wang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models extract useful information from conversation history to enhance the response in long-term conversations. |
| Approach: | They propose a Fragment-then-Compose framework to optimize memory utilization for long-term open-domain conversation. |
| Outcome: | The proposed framework can be used to extract useful information from conversation history . it can be adapted to different situations and improve response generation . |
VideoRAG: Retrieval-Augmented Generation over Video Corpus (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to generating models rely on text and images, but video content is a rich source of multimodal knowledge. |
| Approach: | They propose a framework that dynamically retrieves videos based on their relevance with queries . they use large video language models to represent video content for retrieval . |
| Outcome: | The proposed framework retrieves videos based on relevance with queries and integrates both visual and textual information. |
Thoughts to Target: Enhance Planning for Target-driven Conversation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results demonstrate that our method significantly improves the planning ability of LLMs, especially in target-driven conversations. |
| Approach: | They propose a two-stage framework to improve the LLMs’ capability in planning conversations towards designated targets by distilling natural language plans from a target-driven conversation corpus and generating new plans with demonstration-guided in-context learning. |
| Outcome: | The proposed framework improves the ability of conversational models to plan towards designated targets and can be used to build extensive conversational AI. |
Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing models fail to recall and accurately apply designated persona knowledge without explicit cues . memory-driven role-playing paradigms are attracting significant interest . |
| Approach: | They propose a memory-driven role-playing paradigm that frames persona knowledge as the LLM's internal memory store and a prompting architecture that guides structured memory retrieval and response generation. |
| Outcome: | The proposed paradigm provides a comprehensive diagnostic for four-stage role-playing abilities across 12 LLMs. |
Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models place excessive reasoning burden on response generation, causing performance degradation . we present a new approach that shifts complex reasoning processes from inference to memory construction . |
| Approach: | a new approach shifts complex reasoning processes from inference to memory construction . PREMem extracts fine-grained memory fragments and establishes explicit relationships . small models achieve comparable results even with limited token budgets . |
| Outcome: | The proposed approach reduces the computational burden of response generation by capturing evolution patterns in memory items. |
ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a prior graph-based approach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs. |
| Approach: | They propose a RetrievalEnhanced, Topic-Augmented Graph framework that retrieves relevant summaries from a topic. |
| Outcome: | The proposed framework improves response quality while significantly reducing inference time compared to the baseline. |
Human or LLM as Standardized Patients? A Comparative Study in Medical Education (2026.acl-long)
Copied to clipboard
| Challenge: | Standardized patients (VSPs) are indispensable for clinical skills training but remain expensive and difficult to scale. |
| Approach: | They propose a multi-agent VSP framework that separates case-grounded information disclosure from response generation to support stable, inquiry-conditioned patient behavior. |
| Outcome: | The proposed framework more closely matches human SP behavior than existing VSPs, particularly in case consistency and controlled disclosure. |
IntentionFrame: A Semi-Structured, Multi-Aspect Framework for Fine-Grained Conversational Intention Understanding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for understanding user intentions in multi-turn dialogues fail to capture conversational complexity. |
| Approach: | They propose a semi-structured framework which organizes conversational intents into four interrelated aspects: situation, emotion, action, and knowledge. |
| Outcome: | The proposed framework retains interpretability and provides a rich context to accurately parse and respond to nuanced user inputs. |
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026.findings-acl)
Copied to clipboard
Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns. |
| Approach: | They propose a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. |
| Outcome: | Experiments on LongMemEval and LoCoMo show that the proposed method outperforms existing methods and achieves up to 12.2% improvement in accuracy. |
Prompting the Unknown: Understanding Response Uncertainty in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models are widely used in decision-making across diverse domains. |
| Approach: | They propose a prompt-response concept model that explains the relationship between the amount of task-relevant information provided in the prompt and the LLM-generated response uncertainty by identifying four sources of response uncertainty. |
| Outcome: | The proposed model shows that the amount of information provided in the prompt influences the LLM-generated response uncertainty. |
Retrieval-Augmented Generation with Estimation of Source Reliability (2025.emnlp-main)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs). |
| Approach: | They propose a multi-source RAG framework that estimates the reliability of sources and prioritizes highly reliable and relevant documents. |
| Outcome: | The proposed framework outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. |
Robust In-Context Selection via Online Learned Position-Corrected Attention (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to fix this limitation can be classified into two ways: (1) Methods that use the LLM to generate the selection either via logits of item identifiers, or explicit rank permutations often requiring multiple LLM calls or fine-tuning. |
| Approach: | They propose a method that harnesses attention patterns available from a single forward call on the Large Language Model (LLM) the method learns the logic for item selection using a few in-context examples and a simple online position-debiasing mechanism to correct attention distortion. |
| Outcome: | The proposed method improves selection performance over direct generation and prior attention-based methods while remaining robust to prompt variations and item ordering. |
Empathy Applicability Modeling for General Health Queries (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing NLP frameworks focus on reactively labeling empathy in doctors’ responses but offer limited support for anticipatory modeling of empathy needs, especially in general health queries. |
| Approach: | They propose an Empathy Applicability Framework that classifies patient queries in terms of the applicability of emotional reactions and interpretations based on clinical, contextual, and linguistic cues. |
| Outcome: | The Empathy Applicability Framework outperforms heuristic and zero-shot LLMs in the clinical setting. |
Defending LLMs against Jailbreak Attacks via Template-Based ICL with a Defensive Suffix (2026.findings-acl)
Copied to clipboard
| Challenge: | State-of-the-art large language models (LLMs) are vulnerable to jailbreak attacks, such as GCG and AutoDAN. |
| Approach: | They propose to take the advances of online In-Context Learning and an offline defensive suffix and optimize them using an iterative algorithm and an online stochastic random search to identify the most effective ICL demonstrations. |
| Outcome: | The proposed method reduces attack success rate to nearly *0% while maintaining the model’s utility on benign tasks and incurring only *negligible* computational overhead. |