Papers by Kelong Mao
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmented Generation (2025.findings-naacl)
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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. |
RAG-Studio: Towards In-Domain Adaptation of Retrieval Augmented Generation Through Self-Alignment (2024.findings-emnlp)
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| Challenge: | Existing RAG systems that use pre-trained LLMs and retrievers often fail in specialized domains and applications. |
| Approach: | They propose a self-aligned training framework that adapts general RAG models to specific domains solely through synthetic data. |
| Outcome: | Experiments on specialized domain corpus, general LLM, and general retriever show that the self-aligned training framework outperforms human-annotated training data in specialized fields. |
An Element is Worth a Thousand Words: Enhancing Legal Case Retrieval by Incorporating Legal Elements (2024.findings-acl)
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| Challenge: | Existing methods for legal case retrieval lack the definition of relevance for legal cases . however, the definition goes beyond the common semantic relevance of ad-hoc retrieval. |
| Approach: | They propose a legal element dataset that incorporates legal elements into a semi-automatic method . they propose two models to enhance legal search using legal elements . |
| Outcome: | The proposed models outperform existing methods in enhancing legal search using legal elements. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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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. |
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval (2024.emnlp-main)
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| Challenge: | a conversational search system requires accurate interpretation of user intent from complex multi-turn contexts. |
| Approach: | They propose a dual-learning approach that adapts LLMs for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning. |
| Outcome: | The proposed approach outperforms existing retrieval methods on five conversational search benchmarks. |
Grounding Language Model with Chunking-Free In-Context Retrieval (2024.acl-long)
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| Challenge: | CFIC retrieval approach eliminates the need for document chunking and provides a more efficient and efficient method for RAG systems. |
| Approach: | They propose a Chunking-Free In-Context retrieval approach specifically tailored for RAG systems . they employ auto-aggressive decoding to accurately identify specific evidence text . |
| Outcome: | The proposed method is better than traditional methods on open question answering datasets. |
Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation (2024.emnlp-main)
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| Challenge: | Existing methods for legal case retrieval often overlook the incorporation of legal expert knowledge, leading to unsatisfactory retrieval performance. |
| Approach: | They propose a legal knowledge-guided case reformulation approach based on large language models for effective and interpretable legal case retrieval. |
| Outcome: | The proposed model performs better on complex legal case queries than existing methods. |
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search (2023.findings-emnlp)
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| Challenge: | Existing methods for understanding users’ contextual search intent show unsatisfactory effectiveness and robustness to handle real conversational search scenarios. |
| Approach: | They propose to use large language models to generate multiple query rewrites and hypothetical responses and to aggregate them into an integrated representation that can robustly represent the user’s real contextual search intent. |
| Outcome: | The proposed framework can generate multiple query rewrites and hypothetical responses and can be used to represent the user’s real contextual search intent. |
ConvGQR: Generative Query Reformulation for Conversational Search (2023.acl-long)
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| Challenge: | Existing methods to determine a good search query from the whole conversation context are expensive and often lead to sub-optimal results. |
| Approach: | They propose a framework to reformulate conversational queries based on generative pre-trained language models (PLMs) they propose generative knowledge infusion mechanism to optimize query reformulation and retrieval. |
| Outcome: | Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR. |
Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction (2024.findings-emnlp)
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| Challenge: | Existing large language models (LLMs) underperform in legal judgment prediction due to challenges in understanding case facts and distinguishing between similar charges. |
| Approach: | They propose a framework that allows LLMs to discriminate among charges and a judicial reasoning framework to improve their models for effective legal judgment prediction. |
| Outcome: | The proposed framework improves accuracy and efficiency when dealing with complex and confusing charges. |
ConvTrans: Transforming Web Search Sessions for Conversational Dense Retrieval (2022.emnlp-main)
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| Challenge: | Recent studies show that conversational dense retrieval is a promising technique for realizing conversational search, but its implementation is severely hindered by the lack of data. |
| Approach: | They propose a method that transforms easily-accessible web search sessions into conversational search sessions to alleviate the data scarcity problem. |
| Outcome: | The proposed method can transform easily-accessible web search sessions into conversational search sessions. |
Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding (2024.acl-long)
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| Challenge: | Conversational dense retrieval models lack interpretability, hindering intuitive understanding of model behaviors . a major limitation of conversational dense search is their lack of interpretability . |
| Approach: | They propose to transform opaque session embeddings into explicit interpretable text . they propose to incorporate external interpretable query rewrites into the transformation process . |
| Outcome: | The proposed approach yields more interpretable text and preserves original retrieval performance over baselines. |
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation (2024.acl-long)
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| Challenge: | Existing conversational dense retrieval models view a conversation as a fixed sequence of questions and responses, and these alternate conversations are unrecorded. |
| Approach: | They propose a framework for generalizing Conversational dense retrieval via LLM-cognition data Augmentation (ConvAug) they first generate multi-level augmented conversations to capture the diverse nature of conversational contexts. |
| Outcome: | The proposed framework generalizes Conversational dense retrieval via LLM-cognition data Augmentation on four public datasets. |
Search-Oriented Conversational Query Editing (2023.findings-acl)
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| Challenge: | Existing CQR models are not learned toward improving the downstream search performance . existing models generate the rewrite token-by-token from scratch . |
| Approach: | They propose a text editing-based CQR model tailored for conversational search . they propose rewrite tokens are selected from the dialogue in a non-autoregressive fashion . |
| Outcome: | The proposed model outperforms state-of-the-art models on three conversational search benchmarks while having low rewriting latency. |
CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search (2024.emnlp-main)
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| Challenge: | Recent advances in task-solving capabilities of Large Language Models (LLMs) have motivated researchers to integrate these models into existing conversational search systems. |
| Approach: | They propose a method that leverages the capabilities of large language models to resolve ambiguities in conversation history before query rewriting. |
| Outcome: | The proposed method leads to state-of-the-art results across most settings compared with closed-source LLMs. |
A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression (2025.acl-long)
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| Challenge: | gist-based context compression methods can achieve only slight performance loss on tasks like retrieval-augmented generation and long-document QA, but it faces challenges in tasks like synthetic recall. |
| Approach: | They propose two strategies to improve gist-based context compression in large language models. |
| Outcome: | The proposed methods can achieve only slight performance loss on retrieval-augmented generation and long-document QA tasks, but they face challenges in tasks like synthetic recall. |
History-Aware Conversational Dense Retrieval (2024.findings-acl)
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| Challenge: | Current approaches for conversational dense retrieval rely on fine-tuning a pre-trained ad-hoc retriever, which can be lengthy and noisy. |
| Approach: | They propose a context-denoised query reformulation and automatic mining of supervision signals based on historical turns. |
| Outcome: | The proposed system improves on two public conversational search datasets. |