pwc-archive/papers-with-abstracts huggingface dataset:
LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content
Authors: Jessica Foo, Shaun Khoo
Date: 2024-06-24 00:00:00

As large language models (LLMs) become increasingly prevalent in a wide variety of applications, concerns about the safety of their outputs have become more significant. Most efforts at safety-tuning or moderation today take on a predominantly Western-centric view of safety, especially for toxic, hateful, or violent speech. In this paper, we describe LionGuard, a Singapore-contextualized moderation classifier that can serve as guardrails against unsafe LLM outputs. When assessed on Singlish data, LionGuard outperforms existing widely-used moderation APIs, which are not finetuned for the Singapore context, by 14% (binary) and up to 51% (multi-label). Our work highlights the benefits of localization for moderation classifiers and presents a practical and scalable approach for low-resource languages.
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking
Authors: Zhuoer Wang, Leonardo F. R. Ribeiro, Alexandros Papangelis, Rohan Mukherjee, Tzu-Yen Wang, Xinyan Zhao, Arijit Biswas, James Caverlee, Angeliki Metallinou
Date: 2024-07-18 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Reranking

API call generation is the cornerstone of large language models' tool-using ability that provides access to the larger world. However, existing supervised and in-context learning approaches suffer from high training costs, poor data efficiency, and generated API calls that can be unfaithful to the API documentation and the user's request. To address these limitations, we propose an output-side optimization approach called FANTASE. Two of the unique contributions of FANTASE are its State-Tracked Constrained Decoding (SCD) and Reranking components. SCD dynamically incorporates appropriate API constraints in the form of Token Search Trie for efficient and guaranteed generation faithfulness with respect to the API documentation. The Reranking component efficiently brings in the supervised signal by leveraging a lightweight model as the discriminator to rerank the beam-searched candidate generations of the large language model. We demonstrate the superior performance of FANTASE in API call generation accuracy, inference efficiency, and context efficiency with DSTC8 and API Bank datasets.
BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization
Authors: Ahmed Allam
Date: 2024-07-18 00:00:00

Large Language Models (LLMs) have become pivotal in advancing natural language processing, yet their potential to perpetuate biases poses significant concerns. This paper introduces a new framework employing Direct Preference Optimization (DPO) to mitigate gender, racial, and religious biases in LLM-generated English text. By developing a loss function that favors less biased over biased completions, our approach cultivates a preference for respectful and non-discriminatory language in LLMs. We also contribute a manually designed dataset for training LLMs to recognize and correct biases. This dataset encompasses a diverse range of prompts paired with both biased and unbiased completions. Implementing this approach on the Microsoft Phi-2 model, we demonstrate substantial reductions in biased outputs as our model outperforms the baseline model on almost all bias benchmarks. Our model also achieves better performance compared to other open-source models on most benchmarks. By reducing biases in the language generated by the model, our study marks a significant step towards developing more ethical and socially responsible LLMs. We publicly release BiasDPO dataset on HuggingFace.
High Risk of Political Bias in Black Box Emotion Inference Models
Authors: Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus, Artur Pokropek
Date: 2024-07-18 00:00:00
Tasks: Sentiment Analysis

This paper investigates the presence of political bias in emotion inference models used for sentiment analysis (SA) in social science research. Machine learning models often reflect biases in their training data, impacting the validity of their outcomes. While previous research has highlighted gender and race biases, our study focuses on political bias - an underexplored yet pervasive issue that can skew the interpretation of text data across a wide array of studies. We conducted a bias audit on a Polish sentiment analysis model developed in our lab. By analyzing valence predictions for names and sentences involving Polish politicians, we uncovered systematic differences influenced by political affiliations. Our findings indicate that annotations by human raters propagate political biases into the model's predictions. To mitigate this, we pruned the training dataset of texts mentioning these politicians and observed a reduction in bias, though not its complete elimination. Given the significant implications of political bias in SA, our study emphasizes caution in employing these models for social science research. We recommend a critical examination of SA results and propose using lexicon-based systems as a more ideologically neutral alternative. This paper underscores the necessity for ongoing scrutiny and methodological adjustments to ensure the reliability and impartiality of the use of machine learning in academic and applied contexts.
Learning Goal-Conditioned Representations for Language Reward Models
Authors: Vaskar Nath, Dylan Slack, Jeff Da, Yuntao Ma, Hugh Zhang, Spencer Whitehead, Sean Hendryx
Date: 2024-07-18 00:00:00
Tasks: GSM8K, Math, Reinforcement Learning (RL), Representation Learning

Techniques that learn improved representations via offline data or self-supervised objectives have shown impressive results in traditional reinforcement learning (RL). Nevertheless, it is unclear how improved representation learning can benefit reinforcement learning from human feedback (RLHF) on language models (LMs). In this work, we propose training reward models (RMs) in a contrastive, $\textit{goal-conditioned}$ fashion by increasing the representation similarity of future states along sampled preferred trajectories and decreasing the similarity along randomly sampled dispreferred trajectories. This objective significantly improves RM performance by up to 0.09 AUROC across challenging benchmarks, such as MATH and GSM8k. These findings extend to general alignment as well -- on the Helpful-Harmless dataset, we observe $2.3\%$ increase in accuracy. Beyond improving reward model performance, we show this way of training RM representations enables improved $\textit{steerability}$ because it allows us to evaluate the likelihood of an action achieving a particular goal-state (e.g., whether a solution is correct or helpful). Leveraging this insight, we find that we can filter up to $55\%$ of generated tokens during majority voting by discarding trajectories likely to end up in an "incorrect" state, which leads to significant cost savings. We additionally find that these representations can perform fine-grained control by conditioning on desired future goal-states. For example, we show that steering a Llama 3 model towards helpful generations with our approach improves helpfulness by $9.6\%$ over a supervised-fine-tuning trained baseline. Similarly, steering the model towards complex generations improves complexity by $21.6\%$ over the baseline. Overall, we find that training RMs in this contrastive, goal-conditioned fashion significantly improves performance and enables model steerability.
Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle
Authors: Emman Haider, Daniel Perez-Becker, Thomas Portet, Piyush Madan, Amit Garg, Atabak Ashfaq, David Majercak, Wen Wen, Dongwoo Kim, ZiYi Yang, Jianwen Zhang, Hiteshi Sharma, Blake Bullwinkel, Martin Pouliot, Amanda Minnich, Shiven Chawla, Solianna Herrera, Shahed Warreth, Maggie Engler, Gary Lopez, Nina Chikanov, Raja Sekhar Rao Dheekonda, Bolor-Erdene Jagdagdorj, Roman Lutz, Richard Lundeen, Tori Westerhoff, Pete Bryan, Christian Seifert, Ram Shankar Siva Kumar, Andrew Berkley, Alex Kessler
Date: 2024-07-18 00:00:00
Tasks: Benchmarking, Language Modeling, Language Modelling, Red Teaming

Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone. As these models are deployed in an increasing number of domains, it is critical to ensure that they are aligned with human preferences and safety considerations. In this report, we present our methodology for safety aligning the Phi-3 series of language models. We utilized a "break-fix" cycle, performing multiple rounds of dataset curation, safety post-training, benchmarking, red teaming, and vulnerability identification to cover a variety of harm areas in both single and multi-turn scenarios. Our results indicate that this approach iteratively improved the performance of the Phi-3 models across a wide range of responsible AI benchmarks. Finally, we include additional red teaming strategies and evaluations that were used to test the safety behavior of Phi-3.5-mini and Phi-3.5-MoE, which were optimized for multilingual capabilities.
Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality
Authors: Duy C. Hoang, Hung T. Q. Le, Rui Chu, Ping Li, Weijie Zhao, Yingjie Lao, Khoa D. Doan
Date: 2024-07-17 00:00:00
Tasks: POS

With the widespread adoption of Large Language Models (LLMs), concerns about potential misuse have emerged. To this end, watermarking has been adapted to LLM, enabling a simple and effective way to detect and monitor generated text. However, while the existing methods can differentiate between watermarked and unwatermarked text with high accuracy, they often face a trade-off between the quality of the generated text and the effectiveness of the watermarking process. In this work, we present a novel type of LLM watermark, Sparse Watermark, which aims to mitigate this trade-off by applying watermarks to a small subset of generated tokens distributed across the text. The key strategy involves anchoring watermarked tokens to words that have specific Part-of-Speech (POS) tags. Our experimental results demonstrate that the proposed watermarking scheme achieves high detectability while generating text that outperforms previous LLM watermarking methods in quality across various tasks
Continuous Embedding Attacks via Clipped Inputs in Jailbreaking Large Language Models
Authors: Zihao Xu, Yi Liu, Gelei Deng, Kailong Wang, Yuekang Li, Ling Shi, Stjepan Picek
Date: 2024-07-16 00:00:00

Security concerns for large language models (LLMs) have recently escalated, focusing on thwarting jailbreaking attempts in discrete prompts. However, the exploration of jailbreak vulnerabilities arising from continuous embeddings has been limited, as prior approaches primarily involved appending discrete or continuous suffixes to inputs. Our study presents a novel channel for conducting direct attacks on LLM inputs, eliminating the need for suffix addition or specific questions provided that the desired output is predefined. We additionally observe that extensive iterations often lead to overfitting, characterized by repetition in the output. To counteract this, we propose a simple yet effective strategy named CLIP. Our experiments show that for an input length of 40 at iteration 1000, applying CLIP improves the ASR from 62% to 83%
Hyper-Heuristics Can Profit From Global Variation Operators
Authors: Benjamin Doerr, Johannes F. Lutzeyer
Date: 2024-07-19 00:00:00
Tasks: Evolutionary Algorithms

In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal CLIFF benchmark with remarkable efficiency. The $O(n^3)$ runtime of the MAHH, for almost all cliff widths $d\ge 2,$ is significantly better than the $\Theta(n^d)$ runtime of simple elitist evolutionary algorithms (EAs) on CLIFF. In this work, we first show that this advantage is specific to the CLIFF problem and does not extend to the JUMP benchmark, the most prominent multi-modal benchmark in the theory of randomized search heuristics. We prove that for any choice of the MAHH selection parameter $p$, the expected runtime of the MAHH on a JUMP function with gap size $m = O(n^{1/2})$ is at least $\Omega(n^{2m-1} / (2m-1)!)$. This is significantly slower than the $O(n^m)$ runtime of simple elitist EAs. Encouragingly, we also show that replacing the local one-bit mutation operator in the MAHH with the global bit-wise mutation operator, commonly used in EAs, yields a runtime of $\min\{1, O(\frac{e\ln(n)}{m})^m\} \, O(n^m)$ on JUMP functions. This is at least as good as the runtime of simple elitist EAs. For larger values of $m$, this result proves an asymptotic performance gain over simple EAs. As our proofs reveal, the MAHH profits from its ability to walk through the valley of lower objective values in moderate-size steps, always accepting inferior solutions. This is the first time that such an optimization behavior is proven via mathematical means. Generally, our result shows that combining two ways of coping with local optima, global mutation and accepting inferior solutions, can lead to considerable performance gains.
SHS: Scorpion Hunting Strategy Swarm Algorithm
Authors: Abhilash Singh, Seyed Muhammad Hossein Mousavi, Kumar Gaurav
Date: 2024-07-19 00:00:00
Tasks: Benchmarking

We introduced the Scorpion Hunting Strategy (SHS), a novel population-based, nature-inspired optimisation algorithm. This algorithm draws inspiration from the hunting strategy of scorpions, which identify, locate, and capture their prey using the alpha and beta vibration operators. These operators control the SHS algorithm's exploitation and exploration abilities. To formulate an optimisation method, we mathematically simulate these dynamic events and behaviors. We evaluate the effectiveness of the SHS algorithm by employing 20 benchmark functions (including 10 conventional and 10 CEC2020 functions), using both qualitative and quantitative analyses. Through a comparative analysis with 12 state-of-the-art meta-heuristic algorithms, we demonstrate that the proposed SHS algorithm yields exceptionally promising results. These findings are further supported by statistically significant results obtained through the Wilcoxon rank sum test. Additionally, the ranking of SHS, as determined by the average rank derived from the Friedman test, positions it at the forefront when compared to other algorithms. Going beyond theoretical validation, we showcase the practical utility of the SHS algorithm by applying it to six distinct real-world optimisation tasks. These applications illustrate the algorithm's potential in addressing complex optimisation challenges. In summary, this work not only introduces the innovative SHS algorithm but also substantiates its effectiveness and versatility through rigorous benchmarking and real-world problem-solving scenarios.
LoAS: Fully Temporal-Parallel Dataflow for Dual-Sparse Spiking Neural Networks
Authors: Ruokai Yin, Youngeun Kim, Di wu, Priyadarshini Panda
Date: 2024-07-19 00:00:00

Spiking Neural Networks (SNNs) have gained significant research attention in the last decade due to their potential to drive resource-constrained edge devices. Though existing SNN accelerators offer high efficiency in processing sparse spikes with dense weights, opportunities are less explored in SNNs with sparse weights, i.e., dual-sparsity. In this work, we study the acceleration of dual-sparse SNNs, focusing on their core operation, sparse-matrix-sparse-matrix multiplication (spMspM). We observe that naively running a dual-sparse SNN on existing spMspM accelerators designed for dual-sparse Artificial Neural Networks (ANNs) exhibits sub-optimal efficiency. The main challenge is that processing timesteps, a natural property of SNNs, introduces an extra loop to ANN spMspM, leading to longer latency and more memory traffic. To address the problem, we propose a fully temporal-parallel (FTP) dataflow, which minimizes both data movement across timesteps and the end-to-end latency of dual-sparse SNNs. To maximize the efficiency of FTP dataflow, we propose an FTP-friendly spike compression mechanism that efficiently compresses single-bit spikes and ensures contiguous memory access. We further propose an FTP-friendly inner-join circuit that can lower the cost of the expensive prefix-sum circuits with almost no throughput penalty. All the above techniques for FTP dataflow are encapsulated in LoAS, a Low-latency inference Accelerator for dual-sparse SNNs. With FTP dataflow, compression, and inner-join, running dual-sparse SNN workloads on LoAS demonstrates significant speedup (up to $8.51\times$) and energy reduction (up to $3.68\times$) compared to running it on prior dual-sparse accelerators.
The Effect of Training Schedules on Morphological Robustness and Generalization
Authors: Edoardo Barba, Anil Yaman, Giovanni Iacca
Date: 2024-07-19 00:00:00

Robustness and generalizability are the key properties of artificial neural network (ANN)-based controllers for maintaining a reliable performance in case of changes. It is demonstrated that exposing the ANNs to variations during training processes can improve their robustness and generalization capabilities. However, the way in which this variation is introduced can have a significant impact. In this paper, we define various training schedules to specify how these variations are introduced during an evolutionary learning process. In particular, we focus on morphological robustness and generalizability concerned with finding an ANN-based controller that can provide sufficient performance on a range of physical variations. Then, we perform an extensive analysis of the effect of these training schedules on morphological generalization. Furthermore, we formalize the process of training sample selection (i.e., morphological variations) to improve generalization as a reinforcement learning problem. Overall, our results provide deeper insights into the role of variability and the ways of enhancing the generalization property of evolved ANN-based controllers.
Leveraging Latent Evolutionary Optimization for Targeted Molecule Generation
Authors: Siddartha Reddy N, Sai Prakash MV, Varun V, Vishal Vaddina, Saisubramaniam Gopalakrishnan
Date: 2024-07-02 00:00:00
Tasks: Drug Design, Drug Discovery, Evolutionary Algorithms

Lead optimization is a pivotal task in the drug design phase within the drug discovery lifecycle. The primary objective is to refine the lead compound to meet specific molecular properties for progression to the subsequent phase of development. In this work, we present an innovative approach, Latent Evolutionary Optimization for Molecule Generation (LEOMol), a generative modeling framework for the efficient generation of optimized molecules. LEOMol leverages Evolutionary Algorithms, such as Genetic Algorithm and Differential Evolution, to search the latent space of a Variational AutoEncoder (VAE). This search facilitates the identification of the target molecule distribution within the latent space. Our approach consistently demonstrates superior performance compared to previous state-of-the-art models across a range of constrained molecule generation tasks, outperforming existing models in all four sub-tasks related to property targeting. Additionally, we suggest the importance of including toxicity in the evaluation of generative models. Furthermore, an ablation study underscores the improvements that our approach provides over gradient-based latent space optimization methods. This underscores the effectiveness and superiority of LEOMol in addressing the inherent challenges in constrained molecule generation while emphasizing its potential to propel advancements in drug discovery.
Modified-Improved Fitness Dependent Optimizer for Complex and Engineering Problems
Authors: Hozan K. Hamarashid, Bryar A. Hassan, Tarik A. Rashid
Date: 2024-06-27 00:00:00

Fitness dependent optimizer (FDO) is considered one of the novel swarm intelligent algorithms. Recently, FDO has been enhanced several times to improve its capability. One of the improvements is called improved FDO (IFDO). However, according to the research findings, the variants of FDO are constrained by two primary limitations that have been identified. Firstly, if the number of agents employed falls below five, it significantly diminishes the algorithm's precision. Secondly, the efficacy of FDO is intricately tied to the quantity of search agents utilized. To overcome these limitations, this study proposes a modified version of IFDO, called M-IFDO. The enhancement is conducted by updating the location of the scout bee to the IFDO to move the scout bees to achieve better performance and optimal solutions. More specifically, two parameters in IFDO, which are alignment and cohesion, are removed. Instead, the Lambda parameter is replaced in the place of alignment and cohesion. To verify the performance of the newly introduced algorithm, M-IFDO is tested on 19 basic benchmark functions, 10 IEEE Congress of Evolutionary Computation (CEC-C06 2019), and five real-world problems. M-IFDO is compared against five state-of-the-art algorithms: Improved Fitness Dependent Optimizer (IFDO), Improving Multi-Objective Differential Evolution algorithm (IMODE), Hybrid Sampling Evolution Strategy (HSES), Linear Success-History based Parameter Adaptation for Differential Evolution (LSHADE) and CMA-ES Integrated with an Occasional Restart Strategy and Increasing Population Size and An Iterative Local Search (NBIPOP-aCMAES). The verification criteria are based on how well the algorithm reaches convergence, memory usage, and statistical results. The results show that M-IFDO surpasses its competitors in several cases on the benchmark functions and five real-world problems.
GE2E-AC: Generalized End-to-End Loss Training for Accent Classification
Authors: Chihiro Watanabe, Hirokazu Kameoka
Date: 2024-07-19 00:00:00
Tasks: Accented Speech Recognition, Classification, speech-recognition, Speech Recognition

Accent classification or AC is a task to predict the accent type of an input utterance, and it can be used as a preliminary step toward accented speech recognition and accent conversion. Existing studies have often achieved such classification by training a neural network model to minimize the classification error of the predicted accent label, which can be obtained as a model output. Since we optimize the entire model only from the perspective of classification loss during training time in this approach, the model might learn to predict the accent type from irrelevant features, such as individual speaker identity, which are not informative during test time. To address this problem, we propose a GE2E-AC, in which we train a model to extract accent embedding or AE of an input utterance such that the AEs of the same accent class get closer, instead of directly minimizing the classification loss. We experimentally show the effectiveness of the proposed GE2E-AC, compared to the baseline model trained with the conventional cross-entropy-based loss.