Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, personalized LLM response generation holds the potential to offer substantial benefits for individuals in critical areas such as medical. Existing research has explored memory-augmented methods to prompt the LLM with pre-stored user-specific knowledge for personalized response generation in terms of new queries. We contend that such paradigm is unable to perceive fine-granularity information. In this study, we propose a novel \textbf{M}emory-\textbf{i}njected approach using parameter-efficient fine-tuning (PEFT) and along with a Bayesian Optimisation searching strategy to achieve \textbf{L}LM \textbf{P}ersonalization(\textbf{MiLP}).

Recently, powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users worldwide. However, their strong capabilities and vast world knowledge do not come without associated privacy risks. In this work, we focus on the emerging privacy threat LLMs pose - the ability to accurately infer personal information from online texts. Despite the growing importance of LLM-based author profiling, research in this area has been hampered by a lack of suitable public datasets, largely due to ethical and privacy concerns associated with real personal data. In this work, we take two steps to address this problem: (i) we construct a simulation framework for the popular social media platform Reddit using LLM agents seeded with synthetic personal profiles; (ii) using this framework, we generate SynthPAI, a diverse synthetic dataset of over 7800 comments manually labeled for personal attributes. We validate our dataset with a human study showing that humans barely outperform random guessing on the task of distinguishing our synthetic comments from real ones. Further, we verify that our dataset enables meaningful personal attribute inference research by showing across 18 state-of-the-art LLMs that our synthetic comments allow us to draw the same conclusions as real-world data. Together, this indicates that our dataset and pipeline provide a strong and privacy-preserving basis for future research toward understanding and mitigating the inference-based privacy threats LLMs pose.

There is growing consensus that language model (LM) developers should not be the sole deciders of LM behavior, creating a need for methods that enable the broader public to collectively shape the behavior of LM systems that affect them. To address this need, we present Collective Constitutional AI (CCAI): a multi-stage process for sourcing and integrating public input into LMs-from identifying a target population to sourcing principles to training and evaluating a model. We demonstrate the real-world practicality of this approach by creating what is, to our knowledge, the first LM fine-tuned with collectively sourced public input and evaluating this model against a baseline model trained with established principles from a LM developer. Our quantitative evaluations demonstrate several benefits of our approach: the CCAI-trained model shows lower bias across nine social dimensions compared to the baseline model, while maintaining equivalent performance on language, math, and helpful-harmless evaluations. Qualitative comparisons of the models suggest that the models differ on the basis of their respective constitutions, e.g., when prompted with contentious topics, the CCAI-trained model tends to generate responses that reframe the matter positively instead of a refusal. These results demonstrate a promising, tractable pathway toward publicly informed development of language models.

Understanding commonsense knowledge is crucial in the field of Natural Language Processing (NLP). However, the presence of demographic terms in commonsense knowledge poses a potential risk of compromising the performance of NLP models. This study aims to investigate and propose methods for enhancing the performance and effectiveness of a commonsense polarization classifier by mitigating the influence of demographic terms. Three methods are introduced in this paper: (1) hierarchical generalization of demographic terms (2) threshold-based augmentation and (3) integration of hierarchical generalization and threshold-based augmentation methods (IHTA). The first method involves replacing demographic terms with more general ones based on a term hierarchy ontology, aiming to mitigate the influence of specific terms. To address the limited bias-related information, the second method measures the polarization of demographic terms by comparing the changes in the model's predictions when these terms are masked versus unmasked. This method augments commonsense sentences containing terms with high polarization values by replacing their predicates with synonyms generated by ChatGPT. The third method combines the two approaches, starting with threshold-based augmentation followed by hierarchical generalization. The experiments show that the first method increases the accuracy over the baseline by 2.33%, and the second one by 0.96% over standard augmentation methods. The IHTA techniques yielded an 8.82% and 9.96% higher accuracy than threshold-based and standard augmentation methods, respectively.

Large Language Models (LLMs) have demonstrated impressive capabilities across a range of scientific tasks including mathematics, physics, and chemistry. Despite their successes, the effectiveness of LLMs in handling complex statistical tasks remains systematically under-explored. To bridge this gap, we introduce StatQA, a new benchmark designed for statistical analysis tasks. StatQA comprises 11,623 examples tailored to evaluate LLMs' proficiency in specialized statistical tasks and their applicability assessment capabilities, particularly for hypothesis testing methods. We systematically experiment with representative LLMs using various prompting strategies and show that even state-of-the-art models such as GPT-4o achieve a best performance of only 64.83%, indicating significant room for improvement. Notably, while open-source LLMs (e.g., LLaMA-3) show limited capability, those fine-tuned ones exhibit marked improvements, outperforming all in-context learning-based methods (e.g., GPT-4o). Moreover, our comparative human experiments highlight a striking contrast in error types between LLMs and humans: LLMs primarily make applicability errors, whereas humans mostly make statistical task confusion errors. This divergence highlights distinct areas of proficiency and deficiency, suggesting that combining LLM and human expertise could lead to complementary strengths, inviting further investigation into their collaborative potential.

The growing number of cases that require digital forensic analysis raises concerns about the ability of law enforcement to conduct investigations promptly. Consequently, this paper delves into the potential and effectiveness of integrating Large Language Models (LLMs) into digital forensic investigation to address these challenges. A comprehensive literature review is carried out, encompassing existing digital forensic models, tools, LLMs, deep learning techniques, and the use of LLMs in investigations. The review identifies current challenges within existing digital forensic processes and explores both the obstacles and possibilities of incorporating LLMs. In conclusion, the study asserts that the adoption of LLMs in digital forensics, with appropriate constraints, has the potential to improve investigation efficiency, improve traceability, and alleviate technical and judicial barriers faced by law enforcement entities.

Software robots have long been deployed in Robotic Process Automation (RPA) to automate mundane and repetitive computer tasks. The advent of Large Language Models (LLMs) with advanced reasoning capabilities has set the stage for these agents to now undertake more complex and even previously unseen tasks. However, the LLM-based automation techniques in recent literature frequently rely on HTML source codes for input, limiting their application to web environments. Moreover, the information contained in HTML codes is often inaccurate or incomplete, making the agent less reliable for practical applications. We propose an LLM-based agent that functions solely on the basis of screenshots for recognizing environments, while leveraging in-context learning to eliminate the need for collecting large datasets of human demonstration. Our strategy, named Context-Aware Action Planning (CAAP) prompting encourages the agent to meticulously review the context in various angles. Through our proposed methodology, we achieve a success rate of 94.4% on 67~types of MiniWoB++ problems, utilizing only 1.48~demonstrations per problem type. Our method offers the potential for broader applications, especially for tasks that require inter-application coordination on computers or smartphones, showcasing a significant advancement in the field of automation agents. Codes and models are accessible at https://github.com/caap-agent/caap-agent.

Motion planning in complex scenarios is the core challenge in autonomous driving. Conventional methods apply predefined rules or learn from driving data to plan the future trajectory. Recent methods seek the knowledge preserved in large language models (LLMs) and apply them in the driving scenarios. Despite the promising results, it is still unclear whether the LLM learns the underlying human logic to drive. In this paper, we propose an InstructDriver method to transform LLM into a motion planner with explicit instruction tuning to align its behavior with humans. We derive driving instruction data based on human logic (e.g., do not cause collisions) and traffic rules (e.g., proceed only when green lights). We then employ an interpretable InstructChain module to further reason the final planning reflecting the instructions. Our InstructDriver allows the injection of human rules and learning from driving data, enabling both interpretability and data scalability. Different from existing methods that experimented on closed-loop or simulated settings, we adopt the real-world closed-loop motion planning nuPlan benchmark for better evaluation. InstructDriver demonstrates the effectiveness of the LLM planner in a real-world closed-loop setting. Our code is publicly available at https://github.com/bonbon-rj/InstructDriver.

The advent of pre-trained large language models (LLMs) has revolutionized various natural language processing tasks. These models predominantly employ an auto-regressive decoding mechanism that utilizes Key-Value (KV) caches to eliminate redundant calculations for previous tokens. Nevertheless, as context lengths and batch sizes increase, the linear expansion in memory footprint of KV caches becomes a key bottleneck of LLM deployment, which decreases generation speeds significantly. To mitigate this issue, previous techniques like multi-query attention (MQA) and grouped-query attention (GQA) have been developed, in order to reduce KV heads to accelerate inference with comparable accuracy to multi-head attention (MHA). Despite their effectiveness, existing strategies for compressing MHA often overlook the intrinsic properties of the KV caches. In this work, we explore the low-rank characteristics of the KV caches and propose a novel approach for compressing KV heads. In particular, we carefully optimize the MHA-to-GQA transformation to minimize compression error, and to remain compatible with rotary position embeddings (RoPE), we also introduce specialized strategies for key caches with RoPE. We demonstrate that our method can compress half or even three-quarters of KV heads while maintaining performance comparable to the original LLMs, which presents a promising direction for more efficient LLM deployment in resource-constrained environments.

Recent years have seen two trends emerge simultaneously: large language models (LLMs) trained on increasing amounts of user data (generally scraped indiscriminately from the web), in parallel with increasing legal protections on digital data use including data revocation ("right to be forgotten") laws. In order to support data revocation for models that have already been trained on potentially sensitive data, a number of works have proposed approaches for data "unlearning" (Bourtoule et al., 2021; Gupta et al., 2021; Ginart et al., 2019), which aims to remove the influence of specific subsets of training data without entirely retraining a model. Unlearning in LLMs is particularly challenging because individuals' information may not be contained to specific data points (Brown et al., 2022; Tramèr et al., 2022). Nevertheless, recent work has shown that model finetuning is a promising approach to forget, for example, information corresponding to the book series Harry Potter (Eldan and Russinovich, 2023); information about specific individuals in a synthetic dataset (Maini et al., 2024); or knowledge that could give information to malicious agents Li et al. (2024). While finetuning is a promising approach, a number of recent works have shown that simple modifications to the input prompt or output postprocessing filters (which we collectively call "guardrails") can also be effective for generating a desirable output distribution from a model (Pawelczyk et al., 2023; Brown et al., 2020; Chowdhery et al., 2023; Wei et al., 2021; Kim et al., 2024). Prompt prefixes and postprocessing filters do not update the model weights, so the resulting model itself would not satisfy definitions of unlearning that require the distribution of model weights to match a model retrained from scratch Bourtoule et al. (2021). However, in practical settings where users can only access the model through an API, modifying the output distribution alone can suffice. In fact, most existing unlearning benchmarks (Eldan and Russinovich, 2023; Maini et al., 2024; unl, 2023; Li et al., 2024) only examine the model outputs when evaluating unlearning, which is consistent with a threat model in which users have only API access (see Section 3). In this paper, we investigate how existing benchmarks fare under guardrail-based approaches, and show that in three popular unlearning benchmarks, guardrails not only give strong performance comparable to finetuning baselines, but can also surface weaknesses or inconsistencies in the benchmarks or metrics themselves.