Papers with response quality
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
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| 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. |
The Gutenberg Dialogue Dataset (2021.eacl-main)
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| Challenge: | Current open-domain dialogue datasets offer a trade-off between quality and size . we build a dataset of 14.8M utterances in English and smaller datasets in german, Dutch, Spanish, Portuguese, Italian, and Hungarian . |
| Approach: | They build a high-quality dialogue corpus of 14.8M utterances in English using public-domain books from Project Gutenberg. |
| Outcome: | The proposed datasets show that the extracted dialogues are more accurate and more accurate than the larger Opensubtitles dataset. |
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data (2021.acl-long)
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| 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. |
Token Level Routing Inference System for Edge Devices (2025.acl-demo)
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| Challenge: | Large language models (LLMs) have been gaining in performance but deployment in edge devices faces significant hurdles due to their high computational complexity. |
| Approach: | They propose a collaborative decoding system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. |
| Outcome: | The proposed system achieves 60% performance gain on CommonsenseQA using a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud. |
CAPID: Context-Aware PII Detection for Question-Answering Systems (2026.eacl-srw)
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Mariia Ponomarenko, Sepideh Abedini, Masoumeh Shafieinejad, D. B. Emerson, Shubhankar Mohapatra, Xi He
| Challenge: | Existing approaches mainly redact all PII, disregarding the fact that some may be contextually relevant to the user’s question, resulting in a degradation of response quality. |
| Approach: | They propose a method that fine-tunes a locally owned small language model that filters sensitive information before it is passed to LLMs for QA. |
| Outcome: | The proposed approach outperforms baselines in span, relevance and type accuracy while preserving significantly higher utility under anonymization. |
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)
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| Challenge: | Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents . |
| Approach: | They investigate how entity coreference affects document retrieval and generative performance in RAG-based systems. |
| Outcome: | The proposed model improves QA performance and retrieval relevance and contextual understanding. |
Cross-layer Attention Sharing for Pre-trained Large Language Models (2026.tacl-1)
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Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Jiali Zeng, Qiaozhi He, Murun Yang, Fandong Meng, Jie Zhou, Tong Xiao, Jingbo Zhu
| Challenge: | Existing studies focus on compressing the Key-Value cache or grouping attention heads, while overlooking redundancy between layers. |
| Approach: | They propose a lightweight substitute for self-attention in well-trained LLMs that uses feed-forward networks to align attention heads between adjacent layers and low-rank matrices to approximate differences in layer-wise attention weights. |
| Outcome: | The proposed model reduces redundancy by sharing weights across layers while maintaining high response quality while reducing redundant calculations within 53% 84% of the total layers. |
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions (2024.findings-acl)
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| Challenge: | Large language models respond well in high-resource languages but struggle in low-resourced languages. |
| Approach: | They propose a method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages. |
| Outcome: | The proposed method builds a large-scale cross-lingual instruction tuning dataset on 10 languages. |
Federated Retrieval Augmented Generation for Multi-Product Question Answering (2025.coling-industry)
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| Challenge: | Existing multi-domain RAG-QA approaches query all domains indiscriminately or rely on rigid resource selection. |
| Approach: | They propose a multi-product knowledge-augmented QA framework with probabilistic federated search across domains and relevant knowledge. |
| Outcome: | The proposed framework improves multi-product knowledge-augmented QA performance on Adobe products. |
Rethinking Prompt Optimizers: From Prompt Merits to Optimization (2026.eacl-long)
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Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Lee Onn Mak, Gee Wah Ng, Kezhi Mao
| Challenge: | Existing methods to optimize prompts rely on LLMs' self-generation ability but lack interpretability due to implicit optimization. |
| Approach: | They propose a model-agnostic prompt quality merits and a merit-guided, locally deployable prompt optimizer trained on a lightweight LLM to improve prompt quality. |
| Outcome: | The proposed model avoids online optimization, reduces privacy concerns, and generalizes effectively to both large-scale and lightweight inference models. |
MIRAGE: Metadata-guided Image Retrieval and Answer Generation for E-commerce Troubleshooting (2026.eacl-industry)
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| Challenge: | Existing multimodal systems often associate text and images based on embedding similarity or simple co-location, but fail to ensure that the linked image accurately depicts the specific product or component mentioned in a troubleshooting instruction. |
| Approach: | They propose a metadata-first paradigm that treats structured metadata as a modality for multimodal grounding. |
| Outcome: | The proposed model uses a semantic schema to capture product attributes and visual aspects. |
Incorporating Worker Perspectives into MTurk Annotation Practices for NLP (2023.emnlp-main)
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| Challenge: | Current approaches to data collection for natural language processing on Amazon Mechanical Turk (MTurk) are susceptible to issues regarding workers’ rights and poor response quality without considering the perspectives of MTurq workers. |
| Approach: | They conducted a critical literature review and a survey of MTurk workers to address open questions regarding fair payment, worker privacy, data quality, and considering worker incentives. |
| Outcome: | The findings suggest that future studies may better account for MTurk workers’ experiences in order to respect workers' rights and improve response quality. |
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020.emnlp-main)
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| Challenge: | Recent research shows that dialogue systems trained on human conversation data are biased and can produce responses that reflect people’s gender prejudice. |
| Approach: | They propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance. |
| Outcome: | The proposed framework significantly reduces gender bias in dialogue models while maintaining the response quality. |
Attacker’s Noise Can Manipulate Your Audio-based LLM in the Real World (2026.eacl-long)
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| Challenge: | a recent study examines the real-world vulnerabilities of audio-based large language models such as Qwen2 . despite their impressive capabilities, large language model's are susceptible to various security exploits . |
| Approach: | They investigate the real-world vulnerabilities of audio-based large language models . they show that an adversary can craft stealthy audio perturbations to manipulate ALLMs . |
| Outcome: | The proposed attacks can be adapted to real-world scenarios and impact innocent users . the proposed attacks are scalable and can be exploited in real-time . |
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation (2024.emnlp-main)
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| Challenge: | proprietary large language models (LLMs) have demonstrated impressive code generation performance. |
| Approach: | They propose an adaptive module-based model that refines the direct response distillation process by modular decomposition and adaptive response evolution. |
| Outcome: | The proposed framework outperforms baseline model and code generation methods on three popular benchmarks. |
Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality. |
| Approach: | They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality. |
| Outcome: | The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy. |
Dialogue Language Model with Large-Scale Persona Data Engineering (2025.naacl-industry)
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| Challenge: | Existing persona-consistent dialogue models lack robustness due to limited scale and diversity of datasets. |
| Approach: | They propose an open-domain persona dialogue system that employs extensive generative pre-training on a persona dialog dataset to enhance persona consistency. |
| Outcome: | The proposed model generates vast persona dialogue datasets and addresses invalid persona bias. |
OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance (2026.acl-demo)
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| Challenge: | OpenGlass is an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance . cloud MLLM assistants offer strong visual understanding but often require uploading first-person visual data . |
| Approach: | They propose an open-source system for low-latency multimodal visual assistance . they use an ESP32-based glasses-side unit to capture visual context . |
| Outcome: | The proposed system captures visual context while a nearby device performs local MLLM inference and speech output. |
Beyond the Surface: Measuring Self-Preference in LLM Judgments (2025.emnlp-main)
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| Challenge: | Existing methods measure self-preference bias by comparing the scores a judge model assigns to its own responses with those assigned to other models. |
| Approach: | They propose to use gold judgments as proxies for the actual quality of responses . they propose to measure self-preference bias as the difference between the judge model's own and other models' scores . |
| Outcome: | The proposed method can assess self-preference bias across large language models . it uses gold judgments as proxies for the ground truth scores of the judge model . |
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator (2022.findings-emnlp)
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| Challenge: | Task-oriented dialogue systems are drawing more attention in recent studies . current evaluation methods use annotated utterances in multi-turn dialogue sessions . |
| Approach: | They propose an interactive evaluation framework for task-oriented dialogue (TOD) they build a goal-oriented user simulator based on pre-trained models and use it to generate dialogues . |
| Outcome: | The proposed evaluation framework achieves 98% inform and success rates in the multi-woz dataset and the proposed scores measure the response quality besides the inform and successful rates. |
Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answering (2024.findings-emnlp)
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| Challenge: | Large-scale language models (LLMs) like ChatGPT have demonstrated impressive abilities in generating responses based on human instructions. however, their use in the medical domain can be challenging due to their lack of specific, in-depth knowledge. |
| Approach: | They propose a system that integrates authoritative medical textbooks into LLMs’ framework using plug-and-play modules. |
| Outcome: | The proposed system outperforms the specialized Med-PaLM 2 model on three medical QA tasks by 11.6% to 16.6%. |
Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)
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| Challenge: | Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation. |
| Approach: | They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph. |
| Outcome: | The proposed method performs better than baselines on a large-scale reddit conversation dataset. |
The Colorful Future of LLMs: Evaluating and Improving LLMs as Emotional Supporters for Queer Youth (2024.naacl-long)
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Shir Lissak, Nitay Calderon, Geva Shenkman, Yaakov Ophir, Eyal Fruchter, Anat Brunstein Klomek, Roi Reichart
| Challenge: | Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation. |
| Approach: | They propose a scale that is inspired by psychological standards and expert input to evaluate LLM's interactions with queer-related content. |
| Outcome: | The proposed scale outperforms human responses to queer-related content and outperformed LLMs in the qualitative and quantitative analysis. |
Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning (2025.emnlp-industry)
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| Challenge: | Currently, the dominant end-to-end reinforcement learning paradigm for agents in Large Language Models (LLMs) employs multi-objective optimization that jointly trains both planning and answer summarization capabilities. |
| Approach: | They propose a framework that decouples the training process to enable a focused, single-objective optimization of the planning module. |
| Outcome: | The proposed framework achieves an 8%–12% improvement in planning performance compared to end-to-end baselines. |
Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model (2021.naacl-main)
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| Challenge: | Existing metrics that rely on comparisons to a set of known correct responses do not account for the variety of responses and therefore correlate poorly with human judgment. |
| Approach: | They propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response. |
| Outcome: | The proposed model can be made using unsupervised learning for the next-utterance prediction task on English datasets and shows that using the negative samples alongside random negative samples can increase the model’s correlation with human evaluations. |
Dr. Copilot: A Multi-Agent Prompt Optimized Assistant for Improving Patient-Doctor Communication in Romanian (2025.emnlp-industry)
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| Challenge: | Text-based telemedicine is increasingly common, but quality of medical advice is judged more on how well it is communicated than on its clinical accuracy. |
| Approach: | They propose a multi-agent large language model system that evaluates and enhances the presentation quality of doctor-patient interactions. |
| Outcome: | The proposed system evaluates and improves the presentation quality of Romanian-speaking doctors' written responses. |
Enhancing Reliability in Community Question Answering with an Expert-Oriented RAG System (2026.findings-eacl)
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| Challenge: | Existing LLMs require substantial computational resources and are prone to generating hallucinated or unreliable content. |
| Approach: | They propose an expert-oriented Retrieval-Augmented Generation framework which leverages user modeling to identify archived questions with answers that fully or partially address the user’s new query. |
| Outcome: | The proposed framework synthesizes expert-written answers from similar questions to generate unified answers. |
Bias Fitting to Mitigate Length Bias of Reward Model in RLHF (2026.acl-long)
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| Challenge: | Existing approaches to tackling length bias are limited by their complexity or lack of a linear length-reward relation. |
| Approach: | They propose a framework that learns and corrects underlying bias patterns by fitting a length-reward relationship into a reward model. |
| Outcome: | The proposed framework improves length-controlled win rate and reduces verbosity without compromising performance. |
MEEP: Is this Engaging? Prompting Large Language Models for Dialogue Evaluation in Multilingual Settings (2023.findings-emnlp)
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| Challenge: | Existing metrics for engagingness evaluate the response without the conversation history, are designed for one dataset, or have limited correlation with human annotations. |
| Approach: | They propose to use large language models to evaluate engagingness in dialogue . they propose to include prompts and translated prompts in the model . |
| Outcome: | The proposed model outperforms existing methods on evaluation of engagingness in dialogue across languages. |
\mathsf{Con Instruction}: Universal Jailbreaking of Multimodal Large Language Models via Non-Textual Modalities (2025.acl-long)
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| Challenge: | Existing attacks communicate instruction through text, accompanied by a toxic image or audio . a novel gray-box attack method generates adversarial images or audio to convey harmful instructions to MLLMs . |
| Approach: | They propose a gray-box attack method that generates adversarial images or audio to convey specific harmful instructions to MLLMs by following non-textual instruction. |
| Outcome: | The proposed method achieves highest success rates on visual and audio-language models . larger models are more susceptible toCon Instruction, compared to their underlying models - the results will be released . |
A Dynamic Fusion Model for Consistent Crisis Response (2025.findings-emnlp)
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| Challenge: | a critical yet often overlooked factor is the consistency of response style . few studies have explored methods for maintaining stylistic consistency across generated responses . |
| Approach: | They propose a metric for evaluating style consistency and introduce a method for fusion-based generation . |
| Outcome: | The proposed method outperforms baselines in response quality and stylistic uniformity. |
Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models (2025.coling-main)
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| 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. |
Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots (2021.findings-emnlp)
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| Challenge: | Existing work on retrieval-based chatbots has low-quality affect response . Existing frameworks for obtaining affective response are based on Retrieve-and-Rerank . |
| Approach: | They propose a retrieval-based framework which provides affective response for retrieval chatbots by using a new discriminate-and-rewrite mechanism. |
| Outcome: | The proposed framework outperforms existing baselines and can guarantee the quality of the response and satisfy the affect label. |
Beyond Excess and Deficiency: Adaptive Length Bias Mitigation in Reward Models for RLHF (2025.findings-naacl)
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| Challenge: | Existing efforts to mitigate length bias in reward models have decreased accuracy . achieving an automatic proxy that perfectly replicates human judgment is challenging in practice. |
| Approach: | They propose an adaptive approach that dynamically adjusts the influence of response length in reward evaluations according to the context of the query. |
| Outcome: | The proposed approach reduces unnecessary verbosity while improving overall response quality. |
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs (2025.emnlp-industry)
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Aosong Feng, Balasubramaniam Srinivasan, Yun Zhou, Zhichao Xu, Kang Zhou, Sheng Guan, Yueyan Chen, Xian Wu, Ninad Kulkarni, Yi Zhang, Zhengyuan Shen, Dmitriy Bespalov, Soumya Smruti Mishra, Yifei Teng, Darren Yow-Bang Wang, Haibo Ding, Lin Lee Cheong
| Challenge: | Existing systems require users to manually select models or employ rigid routing rules that fail to capture the continuous spectrum of query complexity. |
| Approach: | They propose a quality-constrained intelligent prompt routing framework that automatically selects optimal models based on predicted response quality and user-specified tolerance levels. |
| Outcome: | The proposed framework achieves 43.9% cost reduction while maintaining quality parity with strongest model in the Claude family and processes requests with sub-150ms latency. |
PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles (2025.naacl-long)
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| Challenge: | Existing research has studied privacy in LLM training data memorization, but it does not prevent users from disclosing PII at inference time. |
| Approach: | They propose a task for chaining API-based and local LLMs that uses public data to construct a benchmark that contains personally identifiable information (PII) |
| Outcome: | The proposed model maintains high response quality for 85.5% of user queries while restricting privacy leakage to only 7.5%. |
Amory: Building Coherent Narrative-Driven Agent Memory through Agentic Reasoning (2026.eacl-long)
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| Challenge: | scalability challenges arise as conversations extend over weeks or months . current approaches fragment conversations into isolated embeddings or graph representations . |
| Approach: | They propose a working memory framework that actively constructs structured memory representations . the framework organizes conversational fragments into episodic narratives based on momentum . |
| Outcome: | Amory improves performance on LOCOMO benchmark while reducing response time by 50%. |
GEAR: A Scalable and Interpretable Evaluation Framework for RAG-Based Car Assistant Systems (2025.emnlp-industry)
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Niloufar Beyranvand, Hamidreza Dastmalchi, Aijun An, Heidar Davoudi, Winston Chan, Ron DiCarlantonio
| Challenge: | Large language models (LLMs) increasingly power car assistants, but evaluating response quality remains a challenge. |
| Approach: | They propose a framework that uses large language models as evaluators to compare assistant responses against ground-truth counterparts. |
| Outcome: | The proposed framework compares assistant responses against ground-truth counterparts, assessing coverage, correctness, and other dimensions of answer quality. |
Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data (2025.naacl-long)
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| Challenge: | Recent efforts to verify text accuracy provide no guarantees on their correctness . a new method to improve LLMs' verifiability is to use quotes to ground models . |
| Approach: | They propose a method that allows models to quote verbatim statements from trusted sources . they leverage a fast membership inference function to verify text against trusted corpora . |
| Outcome: | The proposed method significantly increases verbatim quotes from high-quality documents by up to 130% relative to base models while maintaining response quality. |
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration (D19-1)
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| Challenge: | Experimental results show that restoring incomplete utterances from context improves the performance of open-domain dialogue systems. |
| Approach: | They propose to use a dataset to restore incomplete utterances from context . they propose to pick and combine the data to restore the incomplete . |
| Outcome: | The proposed model significantly boosts response quality of open-domain dialogue systems. |
From Heart to Words: Generating Empathetic Responses via Integrated Figurative Language and Semantic Context Signals (2025.findings-acl)
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| 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. |
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (2024.acl-long)
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Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, Wei Wang
| Challenge: | Existing benchmarks focus on evaluating pure response quality, rather than assessing whether the response follows constraints stated in the instruction. |
| Approach: | They propose a Multi-level Fine-grained Constraints Following Benchmark for Large Language Models that adds a single constraint to the initial instruction at each increased level. |
| Outcome: | The proposed model can follow instructions with more constraints, and is deemed to have better instruction-following ability. |
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)
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| 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 . |
Beyond Length: Context-Aware Expansion and Independence as Developmentally Sensitive Evaluation in Child Utterances (2026.eacl-long)
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| Challenge: | Common proxies such as Mean Length of Utterance (MLU), lexical diversity (vocd-D), and readability indices are dominated by length and ignore conversational context, missing aspects of response quality such as reasoning depth, topic maintenance, and discourse planning. |
| Approach: | They propose a framework that classifies the Previous Adult Utterance Type and scores the child’s response along two axes: Expansion (contextual elaboration and inferential depth) and Independence (the child’ s contribution to advancing the discourse). |
| Outcome: | The proposed framework assesses the child's response along two axes: Expansion (contextual elaboration and inferential depth) and Independence (the child’s contribution to advancing the discourse). |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
Beyond Words: Integrating Theory of Mind into Conversational Agents for Human-Like Belief, Desire, and Intention Alignment (2025.findings-acl)
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| Challenge: | Empirical evaluations of LLaMA-3 models demonstrate that ToM-informed alignment improves response quality, achieving win rates of 63% and 67%, respectively. |
| Approach: | They investigate whether open-source LLaMA models can represent and retain ToM-related constructs and whether they can be used to generate more aligned responses. |
| Outcome: | The proposed models can represent and retain ToM-related constructs and improve response quality. |
Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation (2025.naacl-long)
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| Challenge: | Consistency in the output of language models can vary significantly in style, factual accuracy, and tone, even for similar inputs. |
| Approach: | They propose a decoding algorithm that enhances response consistency across different prompts with no degradation in response quality. |
| Outcome: | The proposed method outperforms standard sampling methods by 10% across semantic and stylistic consistency benchmarks. |
Evaluating Language Models as Synthetic Data Generators (2025.acl-long)
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Seungone Kim, Juyoung Suk, Xiang Yue, Vijay Viswanathan, Seongyun Lee, Yizhong Wang, Kiril Gashteovski, Carolin Lawrence, Sean Welleck, Graham Neubig
| Challenge: | Prior studies have focused on developing effective data generation methods, but lack systematic comparison of different LMs as data generators in a unified setting. |
| Approach: | They propose to use a benchmark to compare language models' data generation abilities against a set of standardized settings and metrics. |
| Outcome: | The proposed benchmark provides standardized settings and metrics to evaluate LMs’ data generation abilities. |
Firewall Routing: Blocking Leads to Better Hybrid Inference for LLMs (2025.emnlp-main)
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| Challenge: | Large language models have significantly enhanced performance across various NLP tasks . high computational costs and latency associated with deploying such models pose bottlenecks . |
| Approach: | They propose a dynamic hybrid inference framework that efficiently selects between a strong and a weak LLM based on the complexity of the query. |
| Outcome: | The proposed method outperforms existing routing strategies by up to 5.29% in APGR . large models often introduce higher latency, making them less suitable for real-time or resource-constrained applications. |
When to Trust LLMs: Aligning Confidence with Response Quality (2024.findings-acl)
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Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie, Qi Cao, Fei Sun, Jinyang Gao, Huawei Shen, Bolin Ding
| Challenge: | Existing methods express reliability by confidence level, but lack objective guidance . Existing approaches express reliability but lack guidance on when to trust LLMs . |
| Approach: | They propose a reward-based approach to align confidence with quality to ensure reliability . they propose 'conqORD' to help model to verbalize greater confidence for higher quality responses . |
| Outcome: | Experiments show that CONQORD significantly improves confidence and response accuracy . the proposed approach can be used to determine reliability of large language models . |
Explaining Length Bias in LLM-Based Preference Evaluations (2025.findings-emnlp)
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Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, Hui Xiong
| Challenge: | a preference evaluation metric is often biased towards longer responses, revealing a reliability problem . a decomposition of the preference evaluation into two components is needed to understand this bias. |
| Approach: | They propose to decompose the preference evaluation metric into two key components . the first component is length-dependent and related to trustworthiness . |
| Outcome: | The proposed evaluation metric is based on two components: desirability and information mass. |
Over-Searching in Search-Augmented Large Language Models (2026.eacl-long)
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| Challenge: | Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. |
| Approach: | They conduct a systematic evaluation of over-searching across multiple dimensions including query types, model categories, retrieval conditions, and multi-turn conversations. |
| Outcome: | The proposed model improves answer accuracy on answerable queries but harms abstention on unanswerable ones . |
Affect-Driven Dialog Generation (N19-1)
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| Challenge: | Existing systems for end-to-end dialog generation focus on response quality without explicit control over affective content of the responses. |
| Approach: | They propose an affect-driven dialog system which generates emotional responses using a continuous representation of emotions. |
| Outcome: | The proposed system outperforms existing systems in terms of BLEU score and response diversity, and qualitative measures. |
Evaluating Coherence in Dialogue Systems using Entailment (N19-1)
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| Challenge: | Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. |
| Approach: | They propose a set of metrics for evaluating topic coherence using distributed sentence representations and calculable approximations of human judgment using conversational coherency. |
| Outcome: | The proposed metrics can be used as a surrogate for human judgment based on conversational coherence on large-scale datasets and provide an unbiased estimate for the quality of the responses. |
Knowledge Graph-Guided Retrieval Augmented Generation (2025.naacl-long)
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| Challenge: | Existing studies on RAG focus on semantic retrieval of isolated relevant chunks, which ignore their intrinsic relationships. |
| Approach: | They propose a framework that utilizes knowledge graphs to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. |
| Outcome: | Extensive experiments on the HotpotQA dataset and its variants demonstrate the advantages of KG2RAG compared to existing RAG-based approaches in terms of response quality and retrieval quality. |
DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented Dialogues (2024.lrec-main)
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| 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. |
Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images (2026.acl-long)
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| Challenge: | Existing methods require explicit safety labels or contrastive data, yet visual inputs enable harmful outputs. |
| Approach: | They propose a visual self-fulfilling alignment mechanism that fine-tunes vision-language models on neutral VQA tasks without any safety labels. |
| Outcome: | The proposed approach reduces attack success rate, improves response quality, and mitigates over-refusal while preserving general capabilities. |
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks (2020.acl-main)
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| Challenge: | Existing methods for training generative models with minimal corpus are difficult . fine-tuning distinguishes tasks from parameter perspective but ignores model-structure perspective . |
| Approach: | They propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting. |
| Outcome: | The proposed method outperforms baselines on two datasets in task consistency, response quality, diversity and consistency. |
MixLLM: Dynamic Routing in Mixed Large Language Models (2025.naacl-long)
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Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen
| Challenge: | Large Language Models (LLMs) exhibit potential artificial generic intelligence, however, their usage is costly with high response latency. |
| Approach: | They develop a dynamic contextual-bandit-based routing system for query-LLM assignment that leverages query tags to enhance query embeddings. |
| Outcome: | The proposed model maximizes response quality and minimizes cost and latency. |
Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review (2026.acl-long)
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| Challenge: | Existing ARG work lacks author inputs and controls and no evaluation measures response reflection of author signals and effectiveness in addressing reviewer concerns. |
| Approach: | They propose a novel author-in-the-loop framework that integrates domain expertise and author-only information into author response generation (ARG) they also propose re3Align, a large-scale dataset of aligned review–response–revision triplets, where revisions proxy author signals and REspGen, an author- in-the loop ARG framework supporting flexible author input, multi-attribute control, and evaluation-guided refinement. |
| Outcome: | Experiments with SOTA LLMs show that author input and evaluation-guided refinement improves author response quality and controllability–quality trade-offs. |
Like a Therapist, But Not: Reddit Narratives of AI in Mental Health Contexts (2026.findings-acl)
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| Challenge: | Large language models are increasingly used for emotional support and mental health–related interactions outside clinical settings. |
| Approach: | They analyze 5,126 Reddit posts describing use of AI for emotional support or therapy . positive sentiment is most strongly associated with task and goal alignment, they say . |
| Outcome: | The proposed framework analyzes language, adoption-related attitudes, and relational alignment at scale. positive sentiment is most strongly associated with task and goal alignment. |
Roleplay-doh: Enabling Domain-Experts to Create LLM-simulated Patients via Eliciting and Adhering to Principles (2024.emnlp-main)
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| Challenge: | Existing methods for improving LLMs in simulations are limited due to privacy concerns and limited domain knowledge. |
| Approach: | They propose a pipeline that elicits qualitative feedback from a domain-expert and transforms it into a set of principles that govern an LLM-prompted roleplay. |
| Outcome: | The proposed pipeline shows a 30% improvement in response quality and principle following for the downstream task. |
PsyAdvisor: A Plug-and-Play Strategy Advice Planner with Proactive Questioning in Psychological Conversations (2025.acl-long)
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| Challenge: | Current psychological LLMs are constrained by passive response mechanisms, limiting their capacity to deploy proactive strategies for psychological counseling. |
| Approach: | They propose a dataset that provides a multi-turn conversation dataset with interpretive labels including strategy decision logic and reaction attribution. |
| Outcome: | The proposed model significantly improves proactive questioning capacity, conversation depth, and response quality. |
ReIFE: Re-evaluating Instruction-Following Evaluation (2025.naacl-long)
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Yixin Liu, Kejian Shi, Alexander Fabbri, Yilun Zhao, PeiFeng Wang, Chien-Sheng Wu, Shafiq Joty, Arman Cohan
| Challenge: | Existing evaluations of large language models (LLMs) for instruction following are incomplete. |
| Approach: | They propose to use 25 base LLMs and 15 recently proposed evaluation protocols to evaluate instruction following on 4 human-annotated datasets. |
| Outcome: | The proposed evaluations identify the best-performing base LLMs and evaluation protocols with a high degree of robustness. |
Grounding Task Assistance with Multimodal Cues from a Single Demonstration (2025.findings-acl)
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Gabriel Herbert Sarch, Balasaravanan Thoravi Kumaravel, Sahithya Ravi, Vibhav Vineet, Andrew D Wilson
| Challenge: | RGB video often fails to capture fine-grained contextual cues such as intent, safety-critical environmental factors, and subtle preferences embedded in human behavior. |
| Approach: | They propose a framework that integrates eye gaze and speech cues to improve conversational agents for task assistance by integrating eye gaze with speech cuests. |
| Outcome: | The proposed framework captures fine-grained intent and user-specific cues, enabling richer contextual grounding for visual question answering. |
Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026.acl-long)
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Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang, Ngai Wong
| Challenge: | Existing studies show that stronger models are not always optimal teachers, suggesting a mismatch between the teacher’s output and the student’s learning ability. |
| Approach: | They propose a method that routes each prompt to its optimal teacher via a query-level router that jointly considers the student models’ learnability and teacher models’ response quality. |
| Outcome: | The proposed method outperforms baselines on six benchmarks including instruct tuning and math reasoning settings. |
An “Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives” (2023.emnlp-main)
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| Challenge: | Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. |
| Approach: | They review 534 papers on building mental health-related conversational agents . they recommend a few recommendations to bridge the disciplinary divide . |
| Outcome: | The systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. |
MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique (2025.findings-emnlp)
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| Challenge: | e MM-CRITIC is a holistic benchmark for evaluating the critique ability of Large Multimodal Models (LMMs) covering 8 main task types and over 500 tasks, covering 4471 samples. |
| Approach: | They introduce a holistic benchmark for evaluating the critique ability of Large Multimodal Models across multiple dimensions: basic, correction, and comparison. |
| Outcome: | The proposed benchmark covers 8 main task types and over 500 tasks and is composed of 4471 samples. |
Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS (2026.findings-acl)
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| Challenge: | Existing systems lack a self-emotion determination mechanism to drive the streaming text-to-speech (TTS) synthesis. |
| Approach: | They propose an emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner. |
| Outcome: | The proposed framework outperforms baselines on DailyDialog, EmoryNLP, IMEOCAP, and MELD on emotional alignment, contextual coherence, and expressive fluency. |
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection (2020.emnlp-main)
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| Challenge: | Existing approaches to learning-to-rank response selection are suboptimal due to ignorance of diversity of response quality. |
| Approach: | They propose to use off-the-shelf response retrieval models as automatic grayscale data generators to train response selection models. |
| Outcome: | The proposed approach can be automated without human effort on grayscale data. |
How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data (2024.emnlp-main)
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Yejie Wang, Keqing He, Dayuan Fu, Zhuoma GongQue, Heyang Xu, Yanxu Chen, Zhexu Wang, Yujia Fu, Guanting Dong, Muxi Diao, Jingang Wang, Mengdi Zhang, Xunliang Cai, Weiran Xu
| Challenge: | Recent research has shown that code pre-trained models improve coding capabilities. |
| Approach: | They propose a code data pruning strategy to identify which datasets are high-quality code instruction data. |
| Outcome: | The proposed model achieves state-of-the-art performance using fewer training data. |
Enhanced Data Synthesis for LLM through Reasoning Structures Generated by Hierarchical GFlowNet (2025.findings-acl)
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| Challenge: | Existing methods to optimize instruction-response pairs lack a systematic design for the underlying reasoning structure. |
| Approach: | They propose a Reasoning Structure driven data Synthesis method that leverages a coarse-to-fine directed acyclic graph to construct reasoning structures efficiently. |
| Outcome: | The proposed method outperforms existing methods in 48.50%, 84.00%, 79.90% of the synthetic datasets trained on the proposed model. |
Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation (2026.acl-long)
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| 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. |
PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants (2025.findings-acl)
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| Challenge: | Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture complexities of personalized task-oriented assistance. |
| Approach: | They propose a benchmark to evaluate personalization in task-oriented AI assistants . the benchmark features user profiles equipped with rich preferences and interaction histories . |
| Outcome: | The proposed benchmark features user profiles equipped with rich preferences and interaction histories . it also features a judge agent and user agent that employs the LLM-as-a-Judge paradigm . |
When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs (2025.acl-long)
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| Challenge: | Existing studies show that adversarial prompts can induce GPTs to leak knowledge file content. |
| Approach: | They propose a workflow inspired by Data Security Posture Management to identify five leakage vectors for knowledge file leakage using 651,022 GPT metadata and 11,820 flows. |
| Outcome: | The proposed workflow analyzes 651,022 GPT metadata, 11,820 flows, and 1,466 responses to identify five leakage vectors: metadata, GPT initialization, retrieval, sandboxed execution environments, and prompts. |
ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning (2025.emnlp-main)
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| Challenge: | Existing methods address data quality via static prefiltering, which decouples quality control from training and fails to mitigate turn-level error propagation. |
| Approach: | They propose an adaptive learning method that dynamically down-weights unreliable supervision without explicit filtering. |
| Outcome: | Experiments on single-source and mixed-quality datasets show improved stability and response quality. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. |
| Approach: | They propose two methods to improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality. |
| Outcome: | The proposed methods improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality. |
Rescorla-Wagner Steering of LLMs for Undesired Behaviors over Disproportionate Inappropriate Context (2025.emnlp-main)
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Rushi Wang, Jiateng Liu, Cheng Qian, Yifan Shen, Yanzhou Pan, Zhaozhuo Xu, Ahmed Abbasi, Heng Ji, Denghui Zhang
| Challenge: | Incorporating external context can enhance the response quality of Large Language Models (LLMs). however, real-world contexts often mix relevant information with disproportionate inappropriate content. |
| Approach: | They propose a Poisoned Context Testbed to pair queries with real-world contexts . they propose 'rw-Steering' to internalize inappropriate signals . |
| Outcome: | The proposed model improves response quality by 39.8% and reverses undesirable behavior curve. |
Verifying the Subjective: Structured Multilingual Rewards for Low-Resource Alignment (2026.findings-acl)
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| Challenge: | Structured Multilingual Reward Modeling Framework extends Reinforcement Learning with Verifiable Rewards (RLVR) to subjective and open-ended tasks. |
| Approach: | They propose a framework that extends Reinforcement Learning with Verifiable Rewards to subjective and open-ended tasks. |
| Outcome: | The proposed framework improves reasoning capability and response quality on 7 tasks across 50 low-resource languages. |
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)
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| Challenge: | Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs. |
| Approach: | They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. |
| Outcome: | The proposed framework outperforms baselines, reducing preference bias and improving response quality. |
ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking (2025.findings-emnlp)
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| 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. |
Reasoning-Guided Exploration for Online DPO (2026.findings-acl)
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| Challenge: | Recent work has aimed to enhance reasoning capabilities of language models, but these methods are limited to domains with objectively verifiable answers. |
| Approach: | They propose a self-play framework to improve reasoning on general-domain data. |
| Outcome: | Experiments show that the proposed framework improves reasoning performance on general-domain data while maintaining competitive performance on verifiable academic benchmarks. |
Enhancing Goal-oriented Proactive Dialogue Systems via Dynamic Multi-dimensional Consistency Optimization (2025.findings-emnlp)
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| Challenge: | Existing work on goal-oriented proactive dialogue systems failed to address the multi-dimensional consistency issue between generated responses and key contextual elements. |
| Approach: | They propose a Dynamic Multi-dimensional Consistency Reinforcement Learning framework which measures the impact of each consistency dimension on overall dialogue quality and provides feedback to improve response quality. |
| Outcome: | The proposed framework significantly improves the consistency of generated responses on two datasets. |
DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)
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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang
| Challenge: | Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. |
| Approach: | They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward. |
| Outcome: | The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench. |
Thinking Twice Makes Large Language Models Safer and More Helpful (2026.findings-acl)
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| Challenge: | Existing safety alignment techniques for large language models (LLMs) struggle to balance harmlessness and usefulness. |
| Approach: | They propose a safety-aware reflection-based reasoning framework that internalizes self-reflective reasoning and encourages reflection and correction. |
| Outcome: | The proposed framework outperforms reasoning-based alignment methods in safety alignment. |
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning (2026.findings-acl)
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Qingfei Zhao, Ruobing Wang, Dingling Xu, Daren Zha, Ma Bowen, Zhichun Wang, Shijie Jia, Limin Liu, Xin Wang
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in multi-step and long-chain reasoning, but extending their reasoning capabilities to encompass deep interactions with search remains a non-trivial challenge. |
| Approach: | They propose a framework for Reasoning–Search integration that integrates multi-reward signals to optimize the reasoning–search interaction trajectories. |
| Outcome: | Experiments on seven datasets show that R-Search significantly outperforms mainstream RAG baselines. |
Alignment Data Map for Efficient Preference Data Selection and Diagnosis (2026.findings-acl)
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| Challenge: | constructing high-quality preference datasets faces scalability challenges due to prohibitive cost and complexity of human annotation. |
| Approach: | They propose a tool to identify and select effective preference data by LLM-as-a-judge, explicit reward model, and reference-based approaches. |
| Outcome: | The proposed tool reduces annotation costs while preserving alignment effectiveness. |