Papers by Shivank Garg
MiSCHiEF: A Benchmark in Minimal-Pairs of Safety and Culture for Holistic Evaluation of Fine-Grained Image-Caption Alignment (2026.eacl-short)
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Sagarika Banerjee, Tangatar Madi, Advait Swaminathan, Jolie Nguyen, Shivank Garg, Kevin Zhu, Vasu Sharma
| Challenge: | Fine-grained image-caption alignment is crucial for vision-language models in socially critical contexts. |
| Approach: | They present a benchmarking dataset for fine-grained image-caption alignment in safety and culture contexts. |
| Outcome: | The proposed benchmarks show that models perform better at confirming correct pairs than rejecting incorrect ones on dual alignment tasks. |
When Prompt Optimization Becomes Jailbreaking: Adaptive Red-Teaming of Large Language Models (2026.eacl-srw)
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| Challenge: | Existing safety evaluations rely on fixed collections of harmful prompts . such attacks span single-shot prompts, multi-turn interactions, cross-lingual settings . |
| Approach: | They propose to use black-box prompt optimization techniques to search for safety failures . they use GPT-5.1 to optimize for a continuous danger score . |
| Outcome: | The proposed approach reduces effective safeguards for large language models . the average danger score of Qwen 3 8B increases from 0.09 in its baseline setting to 0.79 after optimization. |
IPO: Your Language Model is Secretly a Preference Classifier (2025.acl-long)
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| Challenge: | Reinforcement learning from human feedback (RLHF) is the primary method for aligning large language models with human preferences, but it often incurs significant computational and financial costs due to its reliance on training external reward models or human-labeled preferences. |
| Approach: | They propose an alternative approach that leverages generative LLMs as preference classifiers to reduce the dependence on external reward models or human-labeled preferences. |
| Outcome: | The proposed approach reduces the dependence on external reward models or human-labeled preferences by using generative LLMs as preference classifiers. |
The Inner Monologue of Language Models: When Reasoning Traces Reveal More Than They Hide (2026.findings-acl)
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| Challenge: | Recent advances in large language models have enabled them to tackle complex tasks . a fundamental question is: are these models aware of what they "learn" and "think"? |
| Approach: | They define three core competencies: awareness of learned latent policies, generalization of these policies across domains, alignment between internal reasoning traces and final outputs. |
| Outcome: | The results show that RL-trained models exhibit stronger generalizability to novel tasks than SFT models but weak alignment between reasoning traces and final outputs. |