Papers with knowledge selection
Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation (2020.findings-emnlp)
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| Challenge: | Existing knowledge selection models are limited by the context, but the difference between selected knowledge at different turns is often overlooked. |
| Approach: | They propose a difference-aware knowledge selection method that computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. |
| Outcome: | The proposed method outperforms the state-of-the-art methods in a knowledge-grounded dialog. |
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation (2022.coling-1)
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| 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. |
CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations (2022.coling-1)
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| Challenge: | Document-grounded dialogs need smooth transitions between knowledge selected for generating responses. |
| Approach: | They propose a multi-document co-referential graph to capture inter- and intra-document relationships . they propose 'Coref-MDG' method to linearize static Coref-mDG into conversational sequence logic. |
| Outcome: | The proposed method outperforms the state-of-the-art by 9.5%, 7.4% and 8.2% on three public benchmarks. |
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation (2024.lrec-main)
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| 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. |
Unsupervised Knowledge Selection for Dialogue Generation (2021.findings-acl)
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| Challenge: | Existing knowledge selection tasks require the preidentified knowledge to generate informative dialogues. |
| Approach: | They propose a novel method to supervise knowledge selection when the gold knowledge label is unknown by obtaining an oracle knowledge label via distant supervision and leverage knowledge distillation to alleviate the noisy labeling problem of distant supervision. |
| Outcome: | The proposed method outperforms strong supervised baselines on two knowledge-grounded dialogue datasets and generates more informative responses. |
A Compare Aggregate Transformer for Understanding Document-grounded Dialogue (2020.findings-emnlp)
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| 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. |
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning (2022.emnlp-main)
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| Challenge: | Existing methods emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. |
| Approach: | They propose to use a multi-reference dataset to assess the one-to-many efficacy of existing KGC models. |
| Outcome: | The proposed model improves the mapping relationship between multiple knowledge and multiple responses by optimizing the model in a wake-sleep style. |
Semi-supervised Fine-tuning for Large Language Models (2025.findings-naacl)
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| Challenge: | Existing LLMs require labeled data, which can be costly in real-world applications. |
| Approach: | They propose a framework that can fully exploit labeled and unlabeled data for LLM fine-tuning . they conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets . |
| Outcome: | The proposed framework can fully exploit labeled and unlabeled data for LLM alignment from a propagate-and-select manner. |
Generative Knowledge Selection for Knowledge-Grounded Dialogues (2023.findings-eacl)
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| 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. |
CoLV: A Collaborative Latent Variable Model for Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
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| 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. |
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs (D19-1)
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| 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. |
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs (2022.naacl-main)
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| Challenge: | Existing conversation models treat knowledge selection as a sentence ranking problem where each sentence is handled individually, ignoring the internal semantic connection between sentences. |
| Approach: | They propose to automatically convert background knowledge documents into document semantic graphs and perform knowledge selection over such graphs. |
| Outcome: | The proposed model improves on the knowledge selection task and the response generation task on HollE and generalizes on unseen topics in WoW. |
How Does Knowledge Selection Help Retrieval Augmented Generation? (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a powerful method for enhancing natural language generation by integrating external knowledge into a model’s output. |
| Approach: | They empirically analyze how knowledge selection influences downstream generation performance in RAG systems by simulating different retrieval and selection conditions through a controlled mixture of gold and distractor knowledge. |
| Outcome: | The proposed model is based on a controlled mixture of gold and distractor knowledge and simulated with a gold and distractors. |
Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering (2025.naacl-long)
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Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, Pasquale Minervini
| Challenge: | Large language models store factual knowledge in their parameters but their parametric knowledge can conflict with the information provided in the context. |
| Approach: | They propose a training-free representation engineering method that uses pre-trained sparse auto-encoders to control the knowledge selection behaviour of large language models. |
| Outcome: | The proposed method can control the use of both knowledge sources to resolve knowledge conflict in open-domain question-answering tasks surpassing existing representation engineering methods (+10%) and contrastive decoding methods (+5%). |
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022.acl-long)
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| Challenge: | Existing methods for knowledge selection focus on relevance between knowledge and dialogue context, ignoring personal preference for knowledge. |
| Approach: | They propose to introduce personal memory into knowledge selection in chatbots to address personalization issue by integrating personal memory and inverse mapping into a closed loop. |
| Outcome: | The proposed method outperforms existing methods significantly on automatic evaluation and human evaluation. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| 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. |
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack prior and posterior knowledge selection . prior selection module may not learn to select knowledge properly because of lack of posterior information . |
| Approach: | They propose a knowledge distillation-based training strategy to remove the exposure bias of knowledge selection. |
| Outcome: | The proposed model improves on two knowledge-grounded dialogue datasets. |
Well Begun is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Existing knowledge selection methods are costly to learn and difficult to interpret when errors arise in the generated responses. |
| Approach: | They propose a generator-agnostic knowledge selection method to select context-related knowledge among different knowledge structures and variable knowledge requirements. |
| Outcome: | The proposed method can select knowledge accurately in advance and reduce learning, adjustment, and interpretation burden of later models. |
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations (2021.acl-long)
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| Challenge: | Existing methods to train retrieval-based dialogue systems rely on crowd-sourced data . however, it is difficult to collect large-scale dialogues that are grounded on background knowledge . |
| Approach: | They propose to decompose training of knowledge-grounded response selection into three tasks . they propose to combine query-passage matching task with query-dialogue history matching task . |
| Outcome: | Experimental results show that the proposed model can perform comparable to existing methods . the retrieval-based system can leverage background knowledge when conversing with humans . |
Augmenting Knowledge-grounded Conversations with Sequential Knowledge Transition (2021.naacl-main)
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| Challenge: | Existing knowledge-grounded dialogue models lack fine-grained control over knowledge selection and integration with dialogues. |
| Approach: | They propose to explicitly model the knowledge transition in sequential multi-turn conversations by abstracting knowledge into topic tags. |
| Outcome: | The proposed model outperforms baseline models on knowledge-grounded dialogue benchmarks. |
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)
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| Challenge: | Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful. |
| Approach: | They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation. |
| Outcome: | The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses. |
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access (2022.findings-emnlp)
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| 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. |
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment (P19-1)
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| Challenge: | Existing approaches to generate informative responses based on external knowledge are limited to singleround settings. |
| Approach: | They propose a framework for multi-turn conversations with two dialogue agents . they propose to evaluate dialogues on informativeness and coherence . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches significantly on the publicly available dataset. |
Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases (2026.findings-acl)
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| Challenge: | Existing RAG watermarking methods are limited in their encoding capacity and potential degradation of performance or knowledge quality. |
| Approach: | They propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases by benign knowledge completion and a tailored generative watermark algorithm. |
| Outcome: | The proposed method extracts watermarks from adversarial RAGs while remaining stealthy and secure. |
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval (2024.lrec-main)
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| Challenge: | Existing methods for retrieving information from a large corpus of data are sub-optimal and low efficiency. |
| Approach: | They propose a multi-task framework that functions as a universal retriever for three dominant retrieval tasks during the conversation. |
| Outcome: | The proposed framework can perform persona selection, knowledge selection, and response selection tasks simultaneously. |