123 citations · 147 across the 56 of their papers we have counts for
24 papers · 1 filter
Swiss-Knife: A Framework for Reconfigurable Externalised Multi-Objective Alignment at Decode Time
Agnibh Karmakar, Mayur Parvatikar, Shreyash Dhoot +5
Decode-time alignment methods steer a frozen language model by scoring candidate continuations with an external reward and selecting the maximiser. We argue that this shared design…
RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering
Pushwitha Krishnappa, Amit Das, Vinija Jain +2
Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content alignment from surface coincidence (validity…
MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models
Partha Pratim Saha, Samarth Raina, Mayur Parvatikar +4
Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes internally. Aligned systems still fa…
Findings of the Counter Turing Test: AI-Generated Text Detection
Rajarshi Roy, Gurpreet Singh, Ashhar Aziz +16
The growing capability of large language models to produce fluent, contextually coherent text has created mounting pressure on the systems and institutions responsible for ensuring…
Neural FOXP2 -- Language Specific Neuron Steering for Targeted Language Improvement in LLMs
Anusa Saha, Tanmay Joshi, Vinija Jain +2
LLMs are multilingual by training, yet their lingua franca is often English, reflecting English language dominance in pretraining. Other languages remain in parametric memory but a…
Assessing LLM Reliability on Temporally Recent Open-Domain Questions
Pushwitha Krishnappa, Amit Das, Vinija Jain +2
Large Language Models (LLMs) are increasingly deployed for open-domain question answering, yet their alignment with human perspectives on temporally recent information remains unde…
A Comprehensive Dataset for Human vs. AI Generated Text Detection
Rajarshi Roy, Gurpreet Singh, Ashhar Aziz +17
The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authenticity, misinformation, and trustwo…
AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models
Snehasis Mukhopadhyay, Aryan Kasat, Shivam Dubey +5
Large Language Models (LLMs) can inadvertently reflect societal biases present in their training data, leading to harmful or prejudiced outputs. In the Indian context, our empirica…
RADIANT: Retrieval AugmenteD entIty-context AligNmenT -- Introducing RAG-ability and Entity-Context Divergence
Vipula Rawte, Rajarshi Roy, Gurpreet Singh +11
As Large Language Models (LLMs) continue to advance, Retrieval-Augmented Generation (RAG) has emerged as a vital technique to enhance factual accuracy by integrating external knowl…
QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
Danush Khanna, Aditya Kumar Guru, Srivarshinee Sridhar +7
Inference accounts for the majority of latency and energy consumption in large language model (LLM) deployments, often exceeding 90% of total cost. While training-time efficiency h…
Alignment Quality Index (AQI) : Beyond Refusals: AQI as an Intrinsic Alignment Diagnostic via Latent Geometry, Cluster Divergence, and Layer wise Pooled Representations
Abhilekh Borah, Chhavi Sharma, Danush Khanna +12
Alignment is no longer a luxury, it is a necessity. As large language models (LLMs) enter high-stakes domains like education, healthcare, governance, and law, their behavior must r…
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)
Danush Khanna, Gurucharan Marthi Krishna Kumar, Basab Ghosh +5
Adversarial threats against LLMs are escalating faster than current defenses can adapt. We expose a critical geometric blind spot in alignment: adversarial prompts exploit latent c…
LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs
Rushendra Sidibomma, Pransh Patwa, Parth Patwa +3
The detection of hate speech has become increasingly important in combating online hostility and its real-world consequences. Despite recent advancements, there is limited research…
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam +5
Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, comple…
Overview of Factify5WQA: Fact Verification through 5W Question-Answering
Suryavardan Suresh, Anku Rani, Parth Patwa +6
Researchers have found that fake news spreads much times faster than real news. This is a major problem, especially in today's world where social media is the key source of news fo…
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
Ronit Singhal, Pransh Patwa, Parth Patwa +2
Given the widespread dissemination of misinformation on social media, implementing fact-checking mechanisms for online claims is essential. Manually verifying every claim is very c…
Counter Turing Test (): Investigating AI-Generated Text Detection for Hindi -- Ranking LLMs based on Hindi AI Detectability Index ()
Ishan Kavathekar, Anku Rani, Ashmit Chamoli +3
The widespread adoption of Large Language Models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the mi…
FACTOID: FACtual enTailment fOr hallucInation Detection
Vipula Rawte, S. M Towhidul Islam Tonmoy, Krishnav Rajbangshi +4
The widespread adoption of Large Language Models (LLMs) has facilitated numerous benefits. However, hallucination is a significant concern. In response, Retrieval Augmented Generat…
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
Vipula Rawte, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +4
Hallucination has emerged as the most vulnerable aspect of contemporary Large Language Models (LLMs). In this paper, we introduce the Sorry, Come Again (SCA) prompting, aimed to av…
The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey
Saurav Pawar, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +3
The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP), contributing to substantial progress in both text comprehension a…
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain +4
As Large Language Models (LLMs) continue to advance in their ability to write human-like text, a key challenge remains around their tendency to hallucinate generating content that…
SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection
Anku Rani, Dwip Dalal, Shreya Gautam +5
Deception is the intentional practice of twisting information. It is a nuanced societal practice deeply intertwined with human societal evolution, characterized by a multitude of f…
Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index
Megha Chakraborty, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +9
With the rise of prolific ChatGPT, the risk and consequences of AI-generated text has increased alarmingly. To address the inevitable question of ownership attribution for AI-gener…
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti +4
The ability of Large Language Models (LLMs) to encode syntactic and semantic structures of language is well examined in NLP. Additionally, analogy identification, in the form of wo…