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Vijay Lingam

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

ICLR Conference 2025 Conference Paper

Enhancing Language Model Agents using Diversity of Thoughts

Vijay Lingam
Behrooz Omidvar-Tehrani
Sujay Sanghavi
Gaurav Gupta
Sayan Ghosh
Linbo Liu
Jun Huan
Anoop Deoras

A popular approach to building agents using Language Models (LMs) involves iteratively prompting the LM, reflecting on its outputs, and updating the input prompts until the desired task is achieved. However, our analysis reveals two key shortcomings in the existing methods: $(i)$ limited exploration of the decision space due to repetitive reflections, which result in redundant inputs, and $(ii)$ an inability to leverage insights from previously solved tasks. To address these issues, we introduce DoT (Diversity of Thoughts), a novel framework that a) explicitly reduces redundant reflections to enhance decision-space exploration, and b) incorporates a task-agnostic memory component to enable knowledge retrieval from previously solved tasks—unlike current approaches that operate in isolation for each task. Through extensive experiments on a suite of programming benchmarks (HumanEval, MBPP, and LeetCodeHardGym) using a variety of LMs, DoT demonstrates up to a $\textbf{10}$% improvement in Pass@1 while maintaining cost-effectiveness. Furthermore, DoT is modular by design. For instance, when the diverse reflection module of DoT is integrated with existing methods like Tree of Thoughts (ToT), we observe a significant $\textbf{13}$% improvement on Game of 24 (one of the main benchmarks of ToT), highlighting the broad applicability and impact of our contributions across various reasoning tasks.

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ICLR Conference 2024 Conference Paper

Rethinking Label Poisoning for GNNs: Pitfalls and Attacks

Vijay Lingam
Mohammad Sadegh Akhondzadeh
Aleksandar Bojchevski

Node labels for graphs are usually generated using an automated process or crowd-sourced from human users. This opens up avenues for malicious users to compromise the training labels, making it unwise to blindly rely on them. While robustness against noisy labels is an active area of research, there are only a handful of papers in the literature that address this for graph-based data. Even more so, the effects of adversarial label perturbations is sparsely studied. More critically, we reveal that the entire literature on label poisoning for GNNs is plagued by serious evaluation pitfalls. Thus making it hard to conclude how robust GNNs are against label perturbations. After course correcting the state of label poisoning attacks with our faithful evaluation, we identify a discrepancy in attack efficiency of $\sim9\%$ on average. Additionally, we introduce two new simple yet effective attacks that are significantly stronger (up to $\sim8\%$) than the previous strongest attack. Our strongest proposed attack can be efficiently computed and is theoretically backed.

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NeurIPS Conference 2024 Conference Paper

SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors

Vijay Lingam
Atula Tejaswi
Aditya Vavre
Aneesh Shetty
Gautham K. Gudur
Joydeep Ghosh
Alex Dimakis
Eunsol Choi

Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights $$\mathbf{W}$$ and inject learnable matrices $$\mathbf{\Delta W}$$. These $$\mathbf{\Delta W}$$ matrices are structured for efficient parameterization, often using techniques like low-rank approximations or scaling vectors. However, these methods typically exhibit a performance gap compared to full fine-tuning. While recent PEFT methods have narrowed this gap, they do so at the expense of additional learnable parameters. We propose SVFT, a *simple* approach that structures $$\mathbf{\Delta W}$$ based on the specific weight matrix $$\mathbf{W}$$. SVFT updates $$\mathbf{W}$$ as a sparse combination $$M$$ of outer products of its singular vectors, training only the coefficients of these combinations. Crucially, we make additional off-diagonal elements in $M$ learnable, enabling a smooth trade-off between trainable parameters and expressivity—an aspect that distinctly sets our approach apart from previous works leveraging singular values. Extensive experiments on language and vision benchmarks show that SVFT recovers up to **96%** of full fine-tuning performance while training only **0. 006 to 0. 25%** of parameters, outperforming existing methods that achieve only up to **{85\%}** performance with **0. 03 to 0. 8%** of the trainable parameter budget.

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