Divide-Verify-Refine: Aligning LLM Responses with Complex Instructions

Recent studies show that LLMs, particularly open-source models, struggle to follow complex instructions with multiple constraints. Despite the importance, methods to improve LLMs' adherence to such constraints remain unexplored, and current research focuses on evaluating this ability rather than developing solutions. While a few studies enhance constraint adherence through model tuning, this approach is computationally expensive and heavily reliant on training data quality. An alternative is to leverage LLMs' self-correction capabilities, allowing them to adjust responses to better meet specified constraints. However, this self-correction ability of LLMs is limited by the feedback quality, as LLMs cannot autonomously generate reliable feedback or detect errors. Moreover, the self-refinement process heavily depends on few-shot examples that illustrate how to modify responses to meet constraints. As constraints in complex instructions are diverse and vary widely, manually crafting few-shot examples for each constraint type can be labor-intensive and sub-optimal. To deal with these two challenges, we propose the Divide-Verify-Refine (DVR) framework with three steps: (1) Divide complex instructions into single constraints and prepare appropriate tools; (2) Verify: To address the feedback quality problem, these tools will rigorously verify responses and provide reliable feedback; (3) Refine: To address the constraint diversity challenge, we design a refinement repository that collects successful refinement processes and uses them as few-shot demonstrations for future cases, allowing LLMs to learn from the past experience during inference. Additionally, we develop a new dataset of complex instructions, each containing 1-6 constraints. Experiments show that the framework significantly improves performance, doubling LLama3.1-8B's constraint adherence on instructions with 6 constraints.

Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writing

This paper explores the current state of generative AI policies of computer science conferences and offers guidelines for policy adoption.

CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship

The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Furthermore, we devise an extrapolated career reasoning task on TKG for a realistic evaluation. The experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines, two recent TKG reasoning methods, and five state-of-the-art CTP methods in predicting one's future companies and positions-i.e., on average, yielding 6.80% and 34.58% more accurate predictions, respectively.

Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector

As deep vision models' popularity rapidly increases, there is a growing emphasis on explanations for model predictions. The inherently explainable attribution method aims to enhance the understanding of model behavior by identifying the important regions in images that significantly contribute to predictions. It is achieved by cooperatively training a selector (generating an attribution map to identify important features) and a predictor (making predictions using the identified features). Despite many advancements, existing methods suffer from the incompleteness problem, where discriminative features are masked out, and the interlocking problem, where the non-optimized selector initially selects noise, causing the predictor to fit on this noise and perpetuate the cycle. To address these problems, we introduce a new objective that discourages the presence of discriminative features in the masked-out regions thus enhancing the comprehensiveness of feature selection. A pre-trained detector is introduced to detect discriminative features in the masked-out region. If the selector selects noise instead of discriminative features, the detector can observe and break the interlocking situation by penalizing the selector. Extensive experiments show that our model makes accurate predictions with higher accuracy than the regular black-box model, and produces attribution maps with high feature coverage, localization ability, fidelity and robustness. Our code will be available at \href{https://github.com/Zood123/COMET}{https://github.com/Zood123/COMET}.

PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection

Recent literature has highlighted potential risks to academic integrity associated with large language models (LLMs), as they can memorize parts of training instances and reproduce them in the generated texts without proper attribution. In addition, given their capabilities in generating high-quality texts, plagiarists can exploit LLMs to generate realistic paraphrases or summaries indistinguishable from original work. In response to possible malicious use of LLMs in plagiarism, we introduce PlagBench, a comprehensive dataset consisting of 46.5K synthetic plagiarism cases generated using three instruction-tuned LLMs across three writing domains. The quality of PlagBench is ensured through fine-grained automatic evaluation for each type of plagiarism, complemented by human annotation. We then leverage our proposed dataset to evaluate the plagiarism detection performance of five modern LLMs and three specialized plagiarism checkers. Our findings reveal that GPT-3.5 tends to generates paraphrases and summaries of higher quality compared to Llama2 and GPT-4. Despite LLMs' weak performance in summary plagiarism identification, they can surpass current commercial plagiarism detectors. Overall, our results highlight the potential of LLMs to serve as robust plagiarism detection tools.

CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis

The rise of unifying frameworks that enable seamless interoperability of Large Language Models (LLMs) has made LLM-LLM collaboration for open-ended tasks a possibility. Despite this, there have not been efforts to explore such collaborative writing. We take the next step beyond human-LLM collaboration to explore this multi-LLM scenario by generating the first exclusively LLM-generated collaborative stories dataset called CollabStory. We focus on single-author ($N=1$) to multi-author (up to $N=5$) scenarios, where multiple LLMs co-author stories. We generate over 32k stories using open-source instruction-tuned LLMs. Further, we take inspiration from the PAN tasks that have set the standard for human-human multi-author writing tasks and analysis. We extend their authorship-related tasks for multi-LLM settings and present baselines for LLM-LLM collaboration. We find that current baselines are not able to handle this emerging scenario. Thus, CollabStory is a resource that could help propel an understanding as well as the development of techniques to discern the use of multiple LLMs. This is crucial to study in the context of writing tasks since LLM-LLM collaboration could potentially overwhelm ongoing challenges related to plagiarism detection, credit assignment, maintaining academic integrity in educational settings, and addressing copyright infringement concerns. We make our dataset and code available at \texttt{\url{https://github.com/saranya-venkatraman/multi_llm_story_writing}}.

From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models

With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. Text watermarking offers a vital solution, protecting both - LLM-generated and plain text sources. This paper presents a unified overview of different perspectives behind designing watermarking techniques, through a comprehensive survey of the research literature. Our work has two key advantages, (1) we analyze research based on the specific intentions behind different watermarking techniques, evaluation datasets used, watermarking addition, and removal methods to construct a cohesive taxonomy. (2) We highlight the gaps and open challenges in text watermarking to promote research in protecting text authorship. This extensive coverage and detailed analysis sets our work apart, offering valuable insights into the evolving landscape of text watermarking in language models.

ANCHOR: LLM-driven News Subject Conditioning for Text-to-Image Synthesis

Text-to-Image (T2I) Synthesis has made tremendous strides in enhancing synthesized image quality, but current datasets evaluate model performance only on descriptive, instruction-based prompts. Real-world news image captions take a more pragmatic approach, providing high-level situational and Named-Entity (NE) information and limited physical object descriptions, making them abstractive. To evaluate the ability of T2I models to capture intended subjects from news captions, we introduce the Abstractive News Captions with High-level cOntext Representation (ANCHOR) dataset, containing 70K+ samples sourced from 5 different news media organizations. With Large Language Models (LLM) achieving success in language and commonsense reasoning tasks, we explore the ability of different LLMs to identify and understand key subjects from abstractive captions. Our proposed method Subject-Aware Finetuning (SAFE), selects and enhances the representation of key subjects in synthesized images by leveraging LLM-generated subject weights. It also adapts to the domain distribution of news images and captions through custom Domain Fine-tuning, outperforming current T2I baselines on ANCHOR. By launching the ANCHOR dataset, we hope to motivate research in furthering the Natural Language Understanding (NLU) capabilities of T2I models.

Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations

The widespread adoption and transformative effects of large language models (LLMs) have sparked concerns regarding their capacity to produce inaccurate and fictitious content, referred to as `hallucinations'. Given the potential risks associated with hallucinations, humans should be able to identify them. This research aims to understand the human perception of LLM hallucinations by systematically varying the degree of hallucination (genuine, minor hallucination, major hallucination) and examining its interaction with warning (i.e., a warning of potential inaccuracies: absent vs. present). Participants (N=419) from Prolific rated the perceived accuracy and engaged with content (e.g., like, dislike, share) in a Q/A format. Results indicate that humans rank content as truthful in the order genuine > minor hallucination > major hallucination and user engagement behaviors mirror this pattern. More importantly, we observed that warning improves hallucination detection without significantly affecting the perceived truthfulness of genuine content. We conclude by offering insights for future tools to aid human detection of hallucinations.

ALISON: Fast and Effective Stylometric Authorship Obfuscation

Authorship Attribution (AA) and Authorship Obfuscation (AO) are two competing tasks of increasing importance in privacy research. Modern AA leverages an author's consistent writing style to match a text to its author using an AA classifier. AO is the corresponding adversarial task, aiming to modify a text in such a way that its semantics are preserved, yet an AA model cannot correctly infer its authorship. To address privacy concerns raised by state-of-the-art (SOTA) AA methods, new AO methods have been proposed but remain largely impractical to use due to their prohibitively slow training and obfuscation speed, often taking hours. To this challenge, we propose a practical AO method, ALISON, that (1) dramatically reduces training/obfuscation time, demonstrating more than 10x faster obfuscation than SOTA AO methods, (2) achieves better obfuscation success through attacking three transformer-based AA methods on two benchmark datasets, typically performing 15% better than competing methods, (3) does not require direct signals from a target AA classifier during obfuscation, and (4) utilizes unique stylometric features, allowing sound model interpretation for explainable obfuscation. We also demonstrate that ALISON can effectively prevent four SOTA AA methods from accurately determining the authorship of ChatGPT-generated texts, all while minimally changing the original text semantics. To ensure the reproducibility of our findings, our code and data are available at: https://github.com/EricX003/ALISON.