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Makesh Narsimhan Sreedhar

9 accepted papers

2025

AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails

NAACL 2025long

As Large Language Models (LLMs) and generative AI become increasingly widespread, concerns about content safety have grown in parallel. Currently, there is a clear lack of high-quality, human-annotated datasets that address the full spectrum of LLM-related safety risks and are usable for commercial…

2025

Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models

EMNLP 2025

Reasoning-based language models have demonstrated strong performance across various domains, with the most notable gains seen in mathematical and coding tasks. Recent research has shown that reasoning also offers significant benefits for LLM safety and guardrail applications. In this work, we conduc

Cited by 0SourcePDFScholar
2024

HelpSteer 2: Open-source dataset for training top-performing reward models

NeurIPS 2024poster

High-quality preference datasets are essential for training reward models that can effectively guide large language models (LLMs) in generating high-quality responses aligned with human preferences. As LLMs become stronger and better aligned, permissively licensed preference datasets, such as Open A…

2024

HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM

NAACL 2024long

Existing open-source helpfulness preference datasets do not specify what makes some responses more helpful and others less so. Models trained on these datasets can incidentally learn to model dataset artifacts (e.g. preferring longer but unhelpful responses only due to their length). To alleviate th…

Cited by 71SourcePDFScholar
2024

Unsupervised Extraction of Dialogue Policies from Conversations

EMNLP 2024main

Dialogue policies play a crucial role in developing task-oriented dialogue systems, yet their development and maintenance are challenging and typically require substantial effort from experts in dialogue modeling. While in many situations, large amounts of conversational data are available for the t…

Cited by 1SourcePDFScholar
2023

Local Byte Fusion for Neural Machine Translation

ACL 2023long

Subword tokenization schemes are the dominant technique used in current NLP models. However, such schemes can be rigid and tokenizers built on one corpus may not adapt well to other parallel corpora. It has also been observed that in multilingual corpora, subword tokenization schemes oversegment low…

2023

Single Sequence Prediction over Reasoning Graphs for Multi-hop QA

ACL 2023long

Recent generative approaches for multi-hop question answering (QA) utilize the fusion-in-decoder method to generate a single sequence output which includes both a final answer and a reasoning path taken to arrive at that answer, such as passage titles and key facts from those passages. While such mo…

Cited by 10SourcePDFScholar
2023

SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF

EMNLP 2023long findings

Model alignment with human preferences is an essential step in making Large Language Models (LLMs) helpful and consistent with human values. It typically consists of supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) stages. However, RLHF faces inherent limitations st…

Cited by 0SourceScholar
2023

The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models

EMNLP 2023long findings

Compressing large language models (LLMs), often consisting of billions of parameters, provides faster inference, smaller memory footprints, and enables local deployment. The standard compression techniques are pruning and quantization, with the former eliminating redundant connections in model laye…

Cited by 0SourcecodeScholar