8 citations · 24 across the 13 of their papers we have counts for
13 papers
DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels
Zhe Xu, Jiasheng Ye, Xiaoran Liu +8
Recently, significant efforts have been devoted to enhancing the long-context capabilities of Large Language Models (LLMs), particularly in long-context reasoning. To facilitate th…
Unified Active Retrieval for Retrieval Augmented Generation
Qinyuan Cheng, Xiaonan Li, Shimin Li +7
In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Therefore, determining whether to retrieve is crucial…
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
Zhangyue Yin, Qiushi Sun, Qipeng Guo +9
Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances…
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
Jiasheng Ye, Peiju Liu, Tianxiang Sun +3
Pretraining data of large language models composes multiple domains (e.g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome…
Agent Alignment in Evolving Social Norms
Shimin Li, Tianxiang Sun, Qinyuan Cheng +1
Agents based on Large Language Models (LLMs) are increasingly permeating various domains of human production and life, highlighting the importance of aligning them with human value…
Dictionary Learning Improves Patch-Free Circuit Discovery in Mechanistic Interpretability: A Case Study on Othello-GPT
Zhengfu He, Xuyang Ge, Qiong Tang +3
Sparse dictionary learning has been a rapidly growing technique in mechanistic interpretability to attack superposition and extract more human-understandable features from model ac…
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
Siyin Wang, Shimin Li, Tianxiang Sun +6
In the realm of Large Language Models (LLMs), users commonly employ diverse decoding strategies and adjust hyperparameters to control the generated text. However, a critical questi…
Can AI Assistants Know What They Don't Know?
Qinyuan Cheng, Tianxiang Sun, Xiangyang Liu +7
Recently, AI assistants based on large language models (LLMs) show surprising performance in many tasks, such as dialogue, solving math problems, writing code, and using tools. Alt…
SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation
Dong Zhang, Xin Zhang, Jun Zhan +3
Benefiting from effective speech modeling, current Speech Large Language Models (SLLMs) have demonstrated exceptional capabilities in in-context speech generation and efficient gen…
LLatrieval: LLM-Verified Retrieval for Verifiable Generation
Xiaonan Li, Changtai Zhu, Linyang Li +3
Verifiable generation aims to let the large language model (LLM) generate text with supporting documents, which enables the user to flexibly verify the answer and makes the LLM's o…
Flames: Benchmarking Value Alignment of LLMs in Chinese
Kexin Huang, Xiangyang Liu, Qianyu Guo +9
The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, howeve…
Evaluating Hallucinations in Chinese Large Language Models
Qinyuan Cheng, Tianxiang Sun, Wenwei Zhang +8
In this paper, we establish a benchmark named HalluQA (Chinese Hallucination Question-Answering) to measure the hallucination phenomenon in Chinese large language models. HalluQA c…
Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration
Qiushi Sun, Zhangyue Yin, Xiang Li +3
Large Language Models (LLMs) are evolving at an unprecedented pace and have exhibited considerable capability in the realm of natural language processing (NLP) with world knowledge…