Papers by Ananth Balashankar
Identifying Predictive Causal Factors from News Streams (D19-1)
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| Challenge: | Existing word embedding techniques are not suited to learn relationships between words in different documents and contexts. |
| Approach: | They propose a new framework to uncover the relationship between news events and real world phenomena by measuring how word occurrence influences future occurrence. |
| Outcome: | The proposed framework outperforms existing methods in stock price prediction errors for 12 months and 4 years. |
BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes (2026.acl-long)
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| Challenge: | Fine-tuning bias terms of large language models (LLMs) for downstream tasks has gained a lot of attention over the past few years. |
| Approach: | They extensively evaluate bq, bk, v across a wide range of LLMs . they find that bv generally leads to higher downstream performance in low-data regimes compared to bQ and bK . |
| Outcome: | The proposed method improves performance across a wide range of LLMs spanning encoder-only and decoder-free architectures up to 6.7B parameters. |
Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment (2024.emnlp-main)
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| Challenge: | Multilingual human preference data are difficult to obtain at scale, making it challenging to extend this framework to diverse languages. |
| Approach: | They propose a method where a reward model is trained on preference data in one source language and applied to other target languages. |
| Outcome: | The proposed approach is effective under comprehensive evaluation settings, including human evaluation. |
Can We Improve Model Robustness through Secondary Attribute Counterfactuals? (2021.emnlp-main)
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| Challenge: | Recent research has explored how models rely on spurious correlations and how counterfactual data augmentation (CDA) can mitigate such issues. |
| Approach: | They propose a context-aware methodology which takes into account the impact of secondary attributes on the model’s predictions and increases sensitivity for secondary attributes over reweighted counterfactually augmented data. |
| Outcome: | The proposed approach improves sliced accuracy on the original dataset by 7% compared to existing methods and provides guidelines to extend this to other tasks. |
Learning Faithful Representations of Causal Graphs (2021.acl-long)
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| Challenge: | Existing text embeddings that predict direct causal links fail to capture other indirect causal links, leading to spurious correlations in downstream tasks. |
| Approach: | They define faithfulness property of contextual embeddings to capture geometric distance-based properties of directed acyclic causal graphs. |
| Outcome: | The embeddings are 31.3% more faithful to human validated graphs with 800K and 200K causal links and achieve better Precision-Recall AUC in a link prediction fine-tuning task. |
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for finding meaningful counterfactuals rely on human annotation or implicit label invariance . a small amount of human-annotated counterf actual data can generate a robust dataset with learned labels. |
| Approach: | They propose a framework that generates counterfactuals by actively sampling from regions of uncertainty and automatically labeling them with a learned auxiliary classifier. |
| Outcome: | The proposed framework generates a large number of diverse counterfactuals and labels them with a learned classifier. |