5 papers
NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents
Mujahid Sultan, Sri Thuraisamy, Daya Rajaratnam
We present NeuSymMS, an adaptive memory system that enables large language model (LLM) agents to learn, remember, and reason about users across sessions via a hybrid neuro-symbolic…
Confidence-Weighted Token Set Cover for Early Hypothesis Pruning in Self-Consistency
Md Arafat Sultan, Ramón Fernandez Astudillo
Despite its simplicity and efficacy, the high token expenditure of self-consistency can limit its practical utility. Here we investigate if self-consistency can be made more token-…
Optimal Policy Minimum Bayesian Risk
Ramón Fernandez Astudillo, Md Arafat Sultan, Aashka Trivedi +4
Inference scaling helps LLMs solve complex reasoning problems through extended runtime computation. On top of long chain-of-thought (long-CoT) models, purely inference-time techniq…
Self-Refinement of Language Models from External Proxy Metrics Feedback
Keshav Ramji, Young-Suk Lee, Ramón Fernandez Astudillo +5
It is often desirable for Large Language Models (LLMs) to capture multiple objectives when providing a response. In document-grounded response generation, for example, agent respon…
Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversations
Md Arafat Sultan, Jatin Ganhotra, Ramón Fernandez Astudillo
We introduce a structured chain-of-thought (SCoT) prompting approach to generating content-grounded multi-turn question-answer conversations using a pre-trained large language mode…