10 citations · 33 across the 7 of their papers we have counts for
7 papers · 1 filter
How new data permeates LLM knowledge and how to dilute it
Chen Sun, Renat Aksitov, Andrey Zhmoginov +5
Large language models learn and continually learn through the accumulation of gradient-based updates, but how individual pieces of new information affect existing knowledge, leadin…
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Renat Aksitov, Sobhan Miryoosefi, Zonglin Li +10
Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a…
Universal Self-Consistency for Large Language Model Generation
Xinyun Chen, Renat Aksitov, Uri Alon +7
Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled fro…
Hallucination Augmented Recitations for Language Models
Abdullatif Köksal, Renat Aksitov, Chung-Ching Chang
Attribution is a key concept in large language models (LLMs) as it enables control over information sources and enhances the factuality of LLMs. While existing approaches utilize o…
KL-Divergence Guided Temperature Sampling
Chung-Ching Chang, David Reitter, Renat Aksitov +1
Temperature sampling is a conventional approach to diversify large language model predictions. As temperature increases, the prediction becomes diverse but also vulnerable to hallu…
Conversational Recommendation as Retrieval: A Simple, Strong Baseline
Raghav Gupta, Renat Aksitov, Samrat Phatale +3
Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation. However, most CRS approaches do not effectively utilize…
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
Renat Aksitov, Chung-Ching Chang, David Reitter +2
Despite recent progress, it has been difficult to prevent semantic hallucinations in generative Large Language Models. One common solution to this is augmenting LLMs with a retriev…