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Ramaneswaran S

8 accepted papers

2024

A Closer Look at the Limitations of Instruction Tuning

ICML 2024poster

Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trained LLMs into open-domain conversational agents. While IT has achieved notable success and widespread adoption, its limi…

Cited by 18SourcePDFScholar
2024

ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions

ACL 2024long

We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a novel paradigm for generating diverse forms of an input document – we first convert a document into its concise, abstra…

2024

CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP

NAACL 2024findings

We present CoDa (**Co**nstrained Generation based **Da**ta Augmentation), a controllable, effective, and *training-free* data augmentation technique for low-resource (data-scarce) NLP. Our approach is based on prompting off-the-shelf instruction-following Large Language Models (LLMs) for generating…

2024

CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models

ICLR 2024poster

A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representation between audio and language modalities have improved performance in many downstream applications, including zero-shot a…

Cited by 12SourcePDFScholar
2023

ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER

ACL 2023long

Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keyword selection for Conditional Language Model fine-tuning), a novel data augmentation approach based on conditional generat…

2023

DALE: Generative Data Augmentation for Low-Resource Legal NLP

EMNLP 2023long main

We present DALE, a novel and effective generative Data Augmentation framework for low-resource LEgal NLP. DALE addresses the challenges existing frameworks pose in generating effective data augmentations of legal documents - legal language, with its specialized vocabulary and complex semantics, morp…

Cited by 0SourcecodeScholar
2023

From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues

EMNLP 2023long main

Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional d…

Cited by 0SourcecodeScholar
2023

MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization

ACL 2023long

Memes are a powerful tool for communication over social media. Their affinity for evolving across politics, history, and sociocultural phenomena renders them an ideal vehicle for communication. To comprehend the subtle message conveyed within a meme, one must understand the relevant background that…