As large language models (LLMs) are increasingly used across various applications, there is a growing need to control text generation to satisfy specific constraints or requirements. This raises a crucial question: Is it possible to guarantee strict constraint satisfaction in generated outputs while preserving the distribution of the original model as much as possible? We first define the ideal distribution - the one closest to the original model, which also always satisfies the expressed constraint - as the ultimate goal of guaranteed generation. We then state a fundamental limitation, namely that it is impossible to reach that goal through autoregressive training alone. This motivates the necessity of combining training-time and inference-time methods to enforce such guarantees. Based on this insight, we propose GUARD, a simple yet effective approach that combines an autoregressive proposal distribution with rejection sampling. Through GUARD’s theoretical properties, we show how controlling the KL divergence between a specific proposal and the target ideal distribution simultaneously optimizes inference speed and distributional closeness. To validate these theoretical concepts, we conduct extensive experiments on two text generation settings with hard-to-satisfy constraints: a lexical constraint scenario and a sentiment reversal scenario. These experiments show that GUARD achieves perfect constraint satisfaction while almost preserving the ideal distribution with highly improved inference efficiency. GUARD provides a principled approach to enforcing strict guarantees for LLMs without compromising their generative capabilities.
A Character-Centric Creative Story Generation via Imagination
Creative story generation with diverse and detailed story elements is a long-standing goal for large language models. While existing methodologies generate long and coherent stories, they fall significantly short of human capabilities in terms of diversity and character detail. To address this, we introduce a novel story generation framework called CCI (Character-centric Creative story generation via Imagination). CCI features two innovative modules for creative story generation: IG (Image-Guided Imagination) and MW (Multi-Writer model). In the IG module, we utilize DALL-E 3 to create visual representations of key story elements. The IG generates more novel and concrete characters, backgrounds, and main plots than text-only methods. The MW module uses these story elements created by IG to generate multiple description candidates for the protagonist and select the best one. This method incorporates vivid and rich character descriptions into the story. We compared the stories generated by CCI and baseline models through human evaluation and statistical analysis. The results showed significant improvements in the creativity. Furthermore, by enabling interactive multi-modal story generation with users, we have opened up possibilities for human-LLM integration in cultural development.
VLind-Bench: Measuring Language Priors in Large Vision-Language Models
Kang-il Lee , Minbeom Kim, Seunghyun Yoon , Minsung Kim , Dongryeol Lee , Hyukhun Koh , and Kyomin Jung
Large Vision-Language Models (LVLMs) have demonstrated outstanding performance across various multimodal tasks. However, they suffer from a problem known as language prior, where responses are generated based solely on textual patterns while disregarding image information. Addressing the issue of language prior is crucial, as it can lead to undesirable biases or hallucinations when dealing with images that are out of training distribution. Despite its importance, current methods for accurately measuring language priors in LVLMs are poorly studied. Although existing benchmarks based on counterfactual or out-of-distribution images can partially be used to measure language priors, they fail to disentangle language priors from other confounding factors. To this end, we propose a new benchmark called VLind-Bench, which is the first benchmark specifically designed to measure the language priors, or blindness, of LVLMs. It not only includes tests on counterfactual images to assess language priors but also involves a series of tests to evaluate more basic capabilities such as instance comprehension, visual perception, and commonsense biases. For each instance in our benchmark, we ensure that all these basic tests are passed before evaluating the language priors, thereby minimizing the influence of other factors on the assessment. The evaluation and analysis of recent LVLMs in our benchmark reveal that almost all models exhibit a significant reliance on language priors. For each instance in our benchmark, we ensure that all these basic tests are passed before evaluating the language prior, thereby minimizing the influence of other factors on the assessment. The evaluation and analysis of recent LVLMs in our benchmark reveal that almost all models exhibit a significant reliance on language priors.
Mitigating Hallucinations in LVLMs via Summary-Guided Decoding
Kyungmin Min , Minbeom Kim, Kang-il Lee , Dongryeol Lee , and Kyomin Jung
Large Vision-Language Models (LVLMs) have demonstrated impressive performance on multimodal tasks. However, they struggle with object hallucinations due to over-reliance on learned textual patterns, ignoring the provided image. To address this issue, we first investigate language priors in LVLMs. We observe two key findings: (1) Even when predicting image-related part-of-speech (POS) tokens, models increasingly rely on linguistic priors as the token sequences grow, thereby amplifying hallucinations. (2) Methods that directly control LVLM’s output distribution to mitigate language priors can lead to a degradation in text quality or exacerbate hallucinations. Based on these insights, we propose Summary-Guided Decoding (SGD). This method naturally encourages the model to focus more on the image information, with control over only the image-related POS tokens for preserving text quality. Through experiments, we demonstrate that SGD achieves state-of-the-art performance on object hallucination benchmarks. Furthermore, while existing methods show a trade-off between precision and recall, SGD proves to be Pareto optimal in this respect. Lastly, we show that while existing methods suffer from text quality degradation due to such trade-offs, SGD preserves text quality to the maximum extent possible. This paper not only focuses on preventing object hallucination but also presents analysis and solutions aimed at maintaining the original properties of LVLMs.
Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence
Minbeom Kim, Hwanhee Lee , Joonsuk Park , Hwaran Lee† , and Kyomin Jung†
As the integration of large language models into daily life is on the rise, there is a clear gap in benchmarks for advising on subjective and personal dilemmas. To address this, we introduce AdvisorQA, the first benchmark developed to assess LLMs’ capability in offering advice for deeply personalized concerns, utilizing the LifeProTips subreddit forum. This forum features a dynamic interaction where users post advice-seeking questions, receiving an average of 8.9 advice per query, with 164.2 upvotes from hundreds of users, embodying a collective intelligence framework. Therefore, we’ve completed a benchmark encompassing daily life questions, diverse corresponding responses, and majority vote ranking to train our helpfulness metric. Baseline experiments validate the efficacy of AdvisorQA through our helpfulness metric, GPT-4, and human evaluation, analyzing phenomena beyond the trade-off between helpfulness and harmlessness. AdvisorQA marks a significant leap in enhancing QA systems for providing personalized, empathetic advice, showcasing LLMs’ improved understanding of human subjectivity.
LifeTox: Unveiling Implicit Toxicity in Life Advice
Minbeom Kim, Jahyun Koo , Hwanhee Lee , Joonsuk Park† , Hwaran Lee , and Kyomin Jung†
As large language models become increasingly integrated into daily life, detecting implicit toxicity across diverse contexts is crucial. To this end, we introduce LifeTox, a dataset designed for identifying implicit toxicity within a broad range of advice-seeking scenarios. Unlike existing safety datasets, LifeTox comprises diverse contexts derived from personal experiences through open-ended questions. Our experiments demonstrate that RoBERTa fine-tuned on LifeTox matches or surpasses the zero-shot performance of large language models in toxicity classification tasks. These results underscore the efficacy of LifeTox in addressing the complex challenges inherent in implicit toxicity.
2023
Critic-Guided Decoding for Controlled Text Generation
Minbeom Kim, Hwanhee Lee , Kang Min Yoo , Joonsuk Park , Hwaran Lee† , and Kyomin Jung†
Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding. Specifically, we adopt the actor-critic framework and train an LM-steering critic from reward models. Similar to weighted decoding, our method freezes the language model and manipulates the output token distribution using a critic to improve training efficiency and stability. Evaluation of our method on three controlled generation tasks, topic control, sentiment control, and detoxification, shows that our approach generates more coherent and well-controlled texts than previous methods. In addition, CriticControl demonstrates superior generalization ability in zero-shot settings. Human evaluation studies also corroborate our findings.
2022
Action-driven contrastive representation for reinforcement learning
Minbeom Kim, Kyeongha Rho , Yong-duk Kim , and Kyomin Jung†
In reinforcement learning, reward-driven feature learning directly from high-dimensional images faces two challenges: sample-efficiency for solving control tasks and generalization to unseen observations. In prior works, these issues have been addressed through learning representation from pixel inputs. However, their representation faced the limitations of being vulnerable to the high diversity inherent in environments or not taking the characteristics for solving control tasks. To attenuate these phenomena, we propose the novel contrastive representation method, Action-Driven Auxiliary Task (ADAT), which forces a representation to concentrate on essential features for deciding actions and ignore control-irrelevant details. In the augmented state-action dictionary of ADAT, the agent learns representation to maximize agreement between observations sharing the same actions. The proposed method significantly outperforms model-free and model-based algorithms in the Atari and OpenAI ProcGen, widely used benchmarks for sample-efficiency and generalization.
during military service
Learning method for detecting spoofing signal and apparatus for detecting spoofing signal using the same. Minbeom Kim, Youngwha Sung and Jungho Bae Korea Patent. Sep 17. 2020. [Link]
Stacked lossless deconvolutional network for remote sensing image restoration Changyeop shin, Minbeom Kim, Sungho Kim, Youngjung Kim Journal of Applied Remote Sensing 2020
Learning method and apparatus for improved resolution of low resolution satellite images. Changyeop shin, Youngjung Kim, Minbeom Kim and Sungho Kim Korea Patent. July 8. 2019. [Link]
Stacked lossless deconvolutional network for remote sensing image super-resolution Changyeop Shin, Minbeom Kim, Sungho Kim, Youngjung Kim SPIE Image and Signal Processing for Remote Sensing 2019, (Oral)
Deep GPS Spoofing Detection Minbeom Kim, Sungho Kim, Youngjung Kim Korea Institute of Military Science and Technology 2019
NTIRE 2019 Challenge on Real Image Super-Resolution:Methods and Results Jianrui Cai, Minbeom Kim, Youngjung Kim, et al. NTIRE @ CVPR 2019
