Papers by Naren Ramakrishnan
Mitigating Uncertainty in Document Classification (N19-1)
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| Challenge: | Existing models for uncertainty measurement are time-consuming and unable to handle large-scale data sets. |
| Approach: | They propose a new dropout-entropy method for uncertainty measurement and a metric learning method on feature representations to boost the performance of dropout based uncertainty methods. |
| Outcome: | The proposed method improves accuracy from 0.78 to 0.92 when 30% of the most uncertain predictions were handed over to human experts in “20NewsGroup” data. |
Laying Anchors: Semantically Priming Numerals in Language Modeling (2024.findings-naacl)
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| Challenge: | Numeracy is the comprehension of numbers, and numerals are important for comprehension. |
| Approach: | They propose methods to semantically prime numerals by generating anchors governed by the distribution of numeral in any corpus. |
| Outcome: | The proposed methods perform better on numeracy tasks for both in-domain and out-domain numerals. |
CONSENSAGENT: Towards Efficient and Effective Consensus in Multi-Agent LLM Interactions Through Sycophancy Mitigation (2025.findings-acl)
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| Challenge: | Recent advances in multi-agent large language model systems have shown remarkable performance in tasks such as reasoning, planning, and decision-making. |
| Approach: | They propose a framework that dynamically refines prompts based on agent interactions to mitigate sycophancy by requiring additional debate rounds to reach consensus. |
| Outcome: | The proposed framework outperforms both single-agent and multi-a agent baselines and achieves state-of-the-art results across all benchmark datasets. |
Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects (2026.acl-demo)
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Christopher Latimer, Nicolò Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, Naren Ramakrishnan
| Challenge: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Approach: | Hindsight organizes long-term memory into four logical networks and exposes three core operations. |
| Outcome: | Hindsight is a working memory system for AI agents that separates facts from beliefs . the system outperforms existing models on LongMemEval and LoCoMo with 83.6% accuracy . |
Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency (2023.acl-long)
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| Challenge: | Numeracy is the most prevalent form of non-linguistic information embedded in textual corpora. |
| Approach: | They propose a framework for non-linguistic skill injection for LLMs that incorporates information-theoretic interventions and skill-specific losses to enable the learning of strict arithmetic reasoning. |
| Outcome: | The proposed model outperforms the state-of-the-art on injected non-linguistic skills and on linguistic knowledge retention with a fraction of the non-language training data (1/4) and zero additional synthetic linguistic training data. |