INSTITUTIONAL REPOSITORY
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MSQE 1 st year First Semester Semestral Exam 2025 - Microeconomics I
(Indian Statistical Institute, 2025-11-21) Indian Statistical Institute
MSQE 1 st year First Semester Exam 2025 - Mathematical Methods
(Indian Statistical Institute, 2025-11-19) Indian Statistical Institute
A Switch-Point-Aware Contrastive Approach to Sentiment Analysis of Hinglish Code-Mixed Text
(Indian Statistical Institute, 2026-06-15) Sahoo, Prasant Kumar
With the increasing use of social media in non-English-speaking regions, especially in India, people often use Romanized Hindi and English together in their online communication. In a single sentence, they frequently mix Romanized Hindi and English, creating code-mixed text. However, most multilingual transformer models are pre-trained primarily on monolingual data. As a result, NLP systems face challenges when processing code-mixed text, as a single word may be fragmented into meaningless subword pieces, making it difficult for the model to capture its semantic meaning accurately. In this dissertation, we propose a parameter efficient neural architecture consisting of three main components to address these challenges: First, there is a character-level CNN encoder, which handles spelling differences such as "nahi", "nahin", "nah", and "nai" through the chracter n-gram pattern. Next, there is a frozen XLM-R backbone(Conneau
et al., 2019) , the top three layers, which are partly fine-tuned at a slower rate by which it provides rich cross lingual embeddings. Finally, there is a switch-point-aware bilingual gate that spots where the language label switches and blends two adapters using a learned gate weight.During training, it uses Supervised Contrastive Loss to learn better feature representations and Cross-Entropy Loss for classification. Since human annotators agreed on labels only 55% of the time, we use label smoothing to reflect this uncertainty and prevent the model from becoming overly confident in noisy labels. Evaluated on the SentiMix 2020 benchmark(Patwa et al., 2020), our proposed architecture achieves a weighted F1 score of 0.705, which outperforms the baseline model M-BERT (0.654 F1) and is comparable to fully fine-tuned transformer models while requiring only one-tenth of the trainable parameters.Adapter gate visualizations provide interpretable evidence that the gating mechanism captures linguistically meaningful codemixing structure. The architecture is designed to generalize to other code-mixed language pairs through its modular adapter design.
A Study of Prompt Tuning on Small Language Models(SLMs): A Controlled Benchmark and a Lightweight Instance-Aware Method
(2026-06-16) Sahith, Narkadamilli
Parameter-efficient fine-tuning (PEFT) adapts a frozen pre-trained language model by training only a small number of additional parameters. Among PEFT approaches, prompt tuning prepends trainable continuous vectors (soft prompts) to the input. A recurring finding in the literature is that prompt tuning is strongly scale dependent: it rivals full fine-tuning on very large models but lags on smaller ones. This dissertation studies prompt tuning specifically in the small-language-model (SLM) regime. We (i) re-implement a representative set of prompt-tuning methods—Prompt Tuning, P-Tuning v2, LoPT, DPT, DePT, ACCEPT, Residual Prompt Tuning, and PARA—within a single controlled harness, enabling a fair head-to-head comparison against full fine-tuning; (ii) propose IA-DePT, a lightweight instance-aware extension of Decomposed Prompt Tuning that conditions the short soft prompt on each input through a small, zero-initialised gate; and (iii) extend the benchmark beyond a single backbone and task, evaluating the full method suite on six backbone/task settings that span encoder–decoder (t5-small), encoder-only (BERT-base, RoBERTa-base, ELECTRA-small), and decoder-only (DistilGPT-2) architectures across the GLUE/SuperGLUE tasks RTE, WSC, CB, COPA, WiC, and MRPC. On RTE with t5-small, IA-DePT is the strongest parameter-efficient method in our benchmark (55.6% single-seed accuracy) and improves over its own base, DePT, by 6.5 points (53.6% vs. 47.1%, mean over three seeds) while adding only ≈16.9k parameters—a total trainable footprint of 0.05% of the backbone. Because the gate degrades exactly to DePT at initialisation, the comparison is a clean single-variable ablation. The cross-architecture study shows that the instance gate improves on DePT in five of the six settings on each setting’s primary metric (it ties or marginally regresses only on WiC, where every PEFT method sits at chance), so the benefit is broad but not universal. Our analysis characterises the accuracy/parameter trade-offs across method families, the strong effect of task difficulty on the small-model regime, and the role of instance-conditioning, including an honest discussion of why many prompt-tuning methods remain close to the chance baseline at this scale.
Extending UniBreak: Semantic Retrieval and Harmful-Intent Direction Suppression for Token-Level LLM Jailbreaking
(Indian Statistical Institute, 2026-06-15) Saha, Sanket
Token-level adversarial perturbations remain one of the most efficient known attacks against the safety alignment of instruction-tuned large language models (LLMs). Among recent works, the UniBreak framework (You et al., 2026) stands out for unifying gradient-based optimization with an evolutionary perturbation repository. However, its repository relies solely on accumulated success frequency without utilizing query content, and its fitness function implicitly assumes that suppressing refusal tokens is sufficient to elicit harmful responses. In this dissertation, we extend UniBreak along both axes and re-evaluates the framework under stricter generalization and judgment protocols. Specifically, we introduce a semantic perturbation repository that replaces frequency-only repository retrieval and geometric interpolation between historical frequency and sentence-encoder cosine similarity. Furthermore, we use Harmful-Intent Direction Suppression (HIDS) to augment the fitness function by explicitly penalizing the model’s residual-stream projection onto a validated harmful-intent direction. To isolate genuine cross-query generalization from within-dataset memorization, we introduce a two-phase frozen-repository evaluation protocol. Results are evaluated under two complementary judges: a binary classification judge and a 0-10 actionability scoring judge.The scoring judge itself is subsequently analysed through Grad×Input attribution.