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Found 6 papers, 1 papers with code
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Answering complex natural language questions often necessitates multi-step reasoning and integrating external information.
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Question Answering
on Bamboogle
Universal Self-Consistency for Large Language Model Generation
Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled from large language models (LLMs).
Hallucination Augmented Recitations for Language Models
For open book QA as a case study, we demonstrate that models finetuned with our counterfactual datasets improve text grounding, leading to better open book QA performance, with up to an 8. 0% increase in F1 score.
KL-Divergence Guided Temperature Sampling
One common approach to mitigate hallucinations is to provide source/grounding documents and the model is trained to produce predictions that bind to and are attributable to the provided source.
Conversational Recommendation as Retrieval: A Simple, Strong Baseline
Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation.
Characterizing Attribution and Fluency Tradeoffs for Retrieval-Augmented Large Language Models
One common solution to this is augmenting LLMs with a retrieval system and making sure that the generated output is attributable to the retrieved information.
