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20242026
most citedSelf-Retrieval: End-to-End Information Retrieval with One Large Language Model

2 citations · 5 across the 20 of their papers we have counts for

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7 papers · 1 filter

cs.AI2024★ 1 cited

Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering

Xinyan Guan, Yanjiang Liu, Xinyu Lu +8

The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models…

cs.LG2024★ 1 cited

DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models

Ying Zhou, Xinyao Wang, Yulei Niu +6

Recent advancements in large language models (LLMs) have significantly enhanced their knowledge and generative capabilities, leading to a surge of interest in leveraging LLMs for h…

cs.LG2024

Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree?

Xueru Wen, Jie Lou, Yaojie Lu +7

Reward Models (RMs) are crucial for aligning language models with human preferences. Currently, the evaluation of RMs depends on measuring accuracy against a validation set of manu…

cs.AI2024

CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution

Ruiyang Xu, Jialun Cao, Yaojie Lu +6

Code benchmarks such as HumanEval are widely adopted to evaluate Large Language Models' (LLMs) coding capabilities. However, there is an unignorable programming language bias in ex…

cs.CL2024★ 1 cited

On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation

Xueru Wen, Jie Lou, Xinyu Lu +8

Hallucination occurs when large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. To address this critical issue, pr…

cs.CL2024

Towards Scalable Automated Alignment of LLMs: A Survey

Boxi Cao, Keming Lu, Xinyu Lu +10

Alignment is the most critical step in building large language models (LLMs) that meet human needs. With the rapid development of LLMs gradually surpassing human capabilities, trad…

cs.IR2024★ 2 cited

Self-Retrieval: End-to-End Information Retrieval with One Large Language Model

Qiaoyu Tang, Jiawei Chen, Zhuoqun Li +10

The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions be…