2 citations · 4 across the 3 of their papers we have counts for
5 papers · 1 filter
Training with Pseudo-Code for Instruction Following
Prince Kumar, Rudra Murthy, Riyaz Bhat +1
Despite rapid advances in the capabilities of Large Language Models (LLMs), they continue to struggle with following relatively simple and unambiguous instructions, particularly wh…
MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systems
Yannis Katsis, Sara Rosenthal, Kshitij Fadnis +7
Retrieval-augmented generation (RAG) has recently become a very popular task for Large Language Models (LLMs). Evaluating them on multi-turn RAG conversations, where the system is…
Reducing the Scope of Language Models
David Yunis, Siyu Huo, Chulaka Gunasekara +1
Large language models (LLMs) are deployed in a wide variety of user-facing applications. Typically, these deployments have some specific purpose, like answering questions grounded…
KCIF: Knowledge-Conditioned Instruction Following
Rudra Murthy, Praveen Venkateswaran, Prince Kumar +1
LLM evaluation benchmarks have traditionally separated the testing of knowledge/reasoning capabilities from instruction following. In this work, we study the interaction between kn…
Multi-Document Grounded Multi-Turn Synthetic Dialog Generation
Young-Suk Lee, Chulaka Gunasekara, Danish Contractor +2
We introduce a technique for multi-document grounded multi-turn synthetic dialog generation that incorporates three main ideas. First, we control the overall dialog flow using taxo…