papers.bib

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@inproceedings{ghosh2024compare,
      title={Compare without Despair: Reliable Preference Evaluation with Generation Separability},
      author={Ghosh, Sayan and Srinivasan, Tejas and Swayamdipta, Swabha},
      year={2024},
      abbr={EMNLP},
      booktitle={Findings of EMNLP (to appear)},
      url={https://arxiv.org/abs/2407.01878},
      selected=true,
      preview={separability.jpeg},
      abstract={Human evaluation of generated language through pairwise preference judgments is pervasive. However, under common scenarios, such as when generations from a model pair are very similar, or when stochastic decoding results in large variations in generations, it results in inconsistent preference ratings. We address these challenges by introducing a meta-evaluation measure, separability, which estimates how suitable a test instance is for pairwise preference evaluation. For a candidate test instance, separability samples multiple generations from a pair of models, and measures how distinguishable the two sets of generations are. Our experiments show that instances with high separability values yield more consistent preference ratings from both human- and auto-raters. Further, the distribution of separability allows insights into which test benchmarks are more valuable for comparing models. Finally, we incorporate separability into ELO ratings, accounting for how suitable each test instance might be for reliably ranking LLMs. Overall, separability has implications for consistent, efficient and robust preference evaluation of LLMs with both human- and auto-raters.},
}

@inproceedings{ranjit2024oath,
      title={OATH-Frames: Characterizing Online Attitudes Towards Homelessness via LLM Assistants},
      author={Jaspreet Ranjit and Brihi Joshi and Rebecca Dorn and Laura Petry and Olga Koumoundouros and Jayne Bottarini and Peichen Liu and Eric Rice and Swabha Swayamdipta},
      year={2024},
      abbr={EMNLP},
      booktitle={Proceedings of EMNLP (to appear)},
      url={https://arxiv.org/abs/2406.14883},
      code={https://github.com/dill-lab/oath-frames},
      blog={https://dill-lab.github.io/oath-frames/},
      selected=true,
      preview={oath_frames.jpeg},
      selected=true,
      award={Jaspreet received a <a href="https://sites.google.com/usc.edu/showcais-2024/awards?authuser=0">best poster award</a> at USC CAIS's annual symposium, <a href="https://sites.google.com/usc.edu/showcais-2024/">ShowCAIS</a> in Spring 2024.},
      award_name={Best Poster @ ShowCAIS'24},
      code={https://github.com/dill-lab/oath-frames},
      abstract={Homelessness in the U.S. is widespread; individual beliefs and attitudes towards homelessness—often expressed on social media are complex and nuanced (e.g. critical as well as sympathetic). Such attitudes can be challenging to summarize at scale, obfuscating the broader public opinion which advocacy organizations use to guide public policy and reform efforts. Our work proposes an approach to enable a large-scale study on homelessness via two major contributions. First, with the help of domain experts in social work and their trainees, we characterize Online Attitudes towards Homelessness in nine hierarchical frames (OATH-Frames) on a collection of 4K social media posts. Further, in an effort to ease the annotation of these frames, we employ GPT-4 as an LLM assistant to the experts; GPT-4 + Expert annotation presents an attractive trade off owing to a 6.5× speedup in annotation time despite only incurring a 2 point F1 difference in annotation performance. Our effort results in a collection of 8K social media posts labeled by domain and trained experts (with and without GPT-4 assistance). Second, using predicted OATH-Frames on a Flan-T5-Large model trained on our data, we perform a large-scale analysis on 2.4M posts on homelessness. We find that posts that contain mentions of west coast states express more harmful generalizations of people experiencing homelessness (PEH) compared to posts about east coast states. We also find marked differences in attitudes across vulnerable populations as they are compared to PEH as being either more or less deserving of aid.},
}

@inproceedings{gulati2024out,
      title={Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression},
      author={Aryan Gulati and Xingjian Dong and Carlos Hurtado and Sarath Shekkizhar and Swabha Swayamdipta and Antonio Ortega},
      year={2024},
      abbr={EMNLP},
      booktitle={Findings of EMNLP (to appear)},
      url={https://arxiv.org/abs/2407.13141},
      preview={nnk-means.jpg},
      abstract={As language models become more general purpose, increased attention needs to be paid to detecting out-of-distribution (OOD) instances, i.e., those not belonging to any of the distributions seen during training. Existing methods for detecting OOD data are computationally complex and storage-intensive. We propose a novel soft clustering approach for OOD detection based on non-negative kernel regression. Our approach greatly reduces computational and space complexities (up to $11\times $ improvement in inference time and 87% reduction in storage requirements) and outperforms existing approaches by up to 4 AUROC points on four different benchmarks. We also introduce an entropy-constrained version of our algorithm, which leads to further reductions in storage requirements (up to 97% lower than comparable approaches) while retaining competitive performance. Our soft clustering approach for OOD detection highlights its potential for detecting tail-end phenomena in extreme-scale data settings.},
}

@inproceedings{khurana2024crowd,
      title={Crowd-Calibrator: Can Annotator Disagreement Inform Calibration in Subjective Tasks?},
      author={Urja Khurana and Eric Nalisnick and Antske Fokkens and Swabha Swayamdipta},
      year={2024},
      abbr={COLM},
      preview={crowd-calibrator.jpg},
      booktitle={Proceedings of COLM (to appear)},
      abstract={Subjective tasks in NLP have been mostly relegated to objective ones where the gold label is decided by taking the majority vote, thereby obfuscating annotator disagreement and inherent uncertainty of instances. We argue that that subjectivity should play a role in model decisions, considering a selective prediction setting. However, instead of calibrating confidence purely from the model’s perspective, we calibrate models for subjective tasks based on crowdworker agreement. Our method, Crowd-Calibrator, models annotations from crowdworkers and the distance between crowdworker distribution and the model’s own distribution over labels to inform whether the model should abstain from a decision. On two highly subjective tasks, namely hate speech detection and natural language inference (NLI), our experiments show Crowd-Calibrator either outperforming or achieving competitive performance with selective prediction baselines, highlighting the value of bringing in human decision making into model predictions.},
}

@inproceedings{finlayson2024logits,
      title={Logits of API-Protected LLMs Leak Proprietary Information},
      author={Matthew Finlayson and Xiang Ren and Swabha Swayamdipta},
      year={2024},
      abbr={COLM},
      url={https://arxiv.org/abs/2403.09539},
      preview={logits.png},
      selected=true,
      booktitle={Proceedings of COLM (to appear)},
      abstract={The commercialization of large language models (LLMs) has led to the common practice of high-level API-only access to proprietary models. In this work, we show that even with a conservative assumption about the model architecture, it is possible to learn a surprisingly large amount of non-public information about an API-protected LLM from a relatively small number of API queries (e.g., costing under $1,000 for OpenAI's gpt-3.5-turbo). Our findings are centered on one key observation: most modern LLMs suffer from a softmax bottleneck, which restricts the model outputs to a linear subspace of the full output space. We show that this lends itself to a model image or a model signature which unlocks several capabilities with affordable cost: efficiently discovering the LLM's hidden size, obtaining full-vocabulary outputs, detecting and disambiguating different model updates, identifying the source LLM given a single full LLM output, and even estimating the output layer parameters. Our empirical investigations show the effectiveness of our methods, which allow us to estimate the embedding size of OpenAI's gpt-3.5-turbo to be about 4,096. Lastly, we discuss ways that LLM providers can guard against these attacks, as well as how these capabilities can be viewed as a feature (rather than a bug) by allowing for greater transparency and accountability.},
}

@inproceedings{cui2024annotating,
      title={Annotating FrameNet via Structure-Conditioned Language Generation},
      author={Xinyue Cui and Swabha Swayamdipta},
      url={https://arxiv.org/abs/2406.04834},
      abbr={ACL},
      booktitle={Proceedings of ACL (to appear)},
      year={2024},
      selected=true,
      code={https://github.com/dill-lab/FrameNet-Conditional-Generation},
      preview={fn-conditioned-generation.jpeg},
      abstract={Despite the mounting evidence for generative  capabilities of language models in understanding and generating natural language, their effectiveness on explicit manipulation and generation of linguistic structures remain understudied. In this paper, we investigate the task of generating new sentences preserving a given semantic structure, following the FrameNet formalism. We propose a framework to produce novel frame-semantically annotated sentences following an overgenerate-and-filter approach. Our results show that conditioning on rich, explicit semantic information tends to produce generations with high human acceptance, under both prompting and finetuning. Nevertheless, we discover that generated frame-semantic structured data is ineffective at training data augmentation for frame-semantic role labeling. Our study concludes that while generating high-quality, semantically rich data might be within reach, their downstream utility remains to be seen, highlighting the outstanding challenges with automating linguistic annotation tasks.},
}

@inproceedings{finlayson2023closing,
      title={Closing the Curious Case of Neural Text Degeneration},
      author={Matthew Finlayson and John Hewitt and Alexander Koller and Swabha Swayamdipta and Ashish Sabharwal},
      year={2024},
      url={https://arxiv.org/abs/2310.01693},
      abbr={ICLR},
      booktitle={Proc. of ICLR},
      selected=true,
      code={https://github.com/mattf1n/basis-aware-threshold},
      preview={bat.png},
      abstract={Despite their ubiquity in language generation, it remains unknown why truncation sampling heuristics like nucleus sampling are so effective. We provide a theoretical explanation for the effectiveness of the truncation sampling by proving that truncation methods that discard tokens below some probability threshold (the most common type of truncation) can guarantee that all sampled tokens have nonzero true probability. However, thresholds are a coarse heuristic, and necessarily discard some tokens with nonzero true probability as well. In pursuit of a more precise sampling strategy, we show that we can leverage a known source of model errors, the softmax bottleneck, to prove that certain tokens have nonzero true probability, without relying on a threshold. Based on our findings, we develop an experimental truncation strategy and the present pilot studies demonstrating the promise of this type of algorithm. Our evaluations show that our method outperforms its threshold-based counterparts under automatic and human evaluation metrics for low-entropy (i.e., close to greedy) open-ended text generation. Our theoretical findings and pilot experiments provide both insight into why truncation sampling works, and make progress toward more expressive sampling algorithms that better surface the generative capabilities of large language models.}
}

@inproceedings{nam2023does,
      title={Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarization},
      author={Yoonsoo Nam and Adam Lehavi and Daniel Yang and Digbalay Bose and Swabha Swayamdipta and Shrikanth Narayanan},
      year={2024},
      url={https://arxiv.org/abs/2309.09405},
      booktitle={Proc. of ICASSP},
      abbr={ICASSP},
      selected=true,
      preview={video-summarization.png},
      abstract={Video summarization remains a huge challenge in computer vision due to the size of the input videos to be summarized. We propose an efficient, language-only video summarizer that achieves competitive accuracy with high data efficiency. Using only textual captions obtained via a zero-shot approach, we train a language transformer model and forego image representations. This method allows us to perform filtration amongst the representative text vectors and condense the sequence. With our approach, we gain explainability with natural language that comes easily for human interpretation and textual summaries of the videos. An ablation study that focuses on modality and data compression shows that leveraging text modality only effectively reduces input data processing while retaining comparable results.}
}