Ayush Singh

Pratham Singla, Shivank Garg, Ayush Singh et al.

Recent advances in post-training techniques have endowed Large Language Models (LLMs) with enhanced capabilities for tackling complex, logic-intensive tasks through the generation of supplementary planning tokens. This development raises a fundamental question: Are these models aware of what they "learn" and "think"? To address this, we define three core competencies: (1) awareness of learned latent policies, (2) generalization of these policies across domains, and (3) alignment between internal reasoning traces and final outputs. We empirically evaluate these abilities on several tasks, each designed to require learning a distinct policy. Furthermore, we contrast the profiles of models post-trained via Supervised Fine-Tuning (SFT), Direct Policy Optimization (DPO), and Group Relative Policy Optimization (GRPO). Our findings indicate that RL-trained models not only demonstrate greater awareness of their learned behaviors and stronger generalizability to novel, structurally similar tasks than SFT models but also often exhibit weak alignment between their reasoning traces and final outputs, an effect most pronounced in GRPO-trained models.

Ayush Singh, Mansi Gupta, Shivank Garg

Vision Language Models excel in handling a wide range of complex tasks, including Optical Character Recognition (OCR), Visual Question Answering (VQA), and advanced geometric reasoning. However, these models fail to perform well on low-level basic visual tasks which are especially easy for humans. Our goal in this work was to determine if these models are truly "blind" to geometric reasoning or if there are ways to enhance their capabilities in this area. Our work presents a novel automatic pipeline designed to extract key information from images in response to specific questions. Instead of just relying on direct VQA, we use question-derived keywords to create a caption that highlights important details in the image related to the question. This caption is then used by a language model to provide a precise answer to the question without requiring external fine-tuning.

Ruiyang Xu, Ayush Singh

Natural interface to database (NLIDB) has been researched a lot during the past decades. In the core of NLIDB, is a semantic parser used to convert natural language into SQL. Solutions from traditional NLP methodology focuses on grammar rule pattern learning and pairing via intermediate logic forms. Although those methods give an acceptable performance on certain specific database and parsing tasks, they are hard to generalize and scale. On the other hand, recent progress in neural deep learning seems to provide a promising direction towards building a general NLIDB system. Unlike the traditional approach, those neural methodologies treat the parsing problem as a sequence-to-sequence learning problem. In this paper, we experimented on several sequence-to-sequence learning models and evaluate their performance on general database parsing task.

Ayush Singh, Ritu Palod

Expressing in language is subjective. Everyone has a different style of reading and writing, apparently it all boil downs to the way their mind understands things (in a specific format). Language style transfer is a way to preserve the meaning of a text and change the way it is expressed. Progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data, use cases, and reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose a model combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of evaluating style transfer tasks, we frame the problem as sentiment transfer and evaluation using a sentiment classifier to calculate how many sentiments was the model able to transfer. We report our results on several kinds of models.