CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models

Authors: junho  kim
hyun jun kim
yeon  ju
kim  yong
man  ro
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-13 18:36:36

Abstract: Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation. However, alongside these advancements, the issue of hallucinations has emerged as a significant challenge, producing erroneous responses that are unrelated to the visual contents. In this paper, we introduce a novel contrastive-based decoding method, COuntering DEscription Contrastive Decoding (CODE), which leverages self-generated descriptions as contrasting references during the decoding phase of LMMs to address hallucination issues. CODE utilizes the comprehensive descriptions from model itself as visual counterpart to correct and improve response alignment with actual visual content. By dynamically adjusting the information flow and distribution of next-token predictions in the LMM's vocabulary, CODE enhances the coherence and informativeness of generated responses. Extensive experiments demonstrate that our method significantly reduces hallucinations and improves cross-modal consistency across various benchmarks and cutting-edge LMMs. Our method provides a simple yet effective decoding strategy that can be integrated to existing LMM frameworks without additional training.

Main paper: 
 introduction: 
 With recent advancements of Large Language Models (LLMs) [18, 4, 54, 11, 46] , Large Multi-modal Models (LMMs), sometimes referred as Large Vision-Language Models [24, 63, 32, 31] , have been drawn great attention for their natural multi-modal interaction with users through back-and-forth conversations. Leveraging their robust generation capabilities, various pioneering tasks in pre-LMM era such as image captioning [7, 58, 27] , visual question answering [1, 2] , object detection [17] , etc., have been integrated into a single task rather than treated as sub-tasks and achieved significant milestones [21, 59, 44] . However, at the same time, the hallucination issue [49, 67] has become one of the emerging problems when adopting LMMs into real-world applications due to their potential spurious generation in critical areas.Here, unlike hallucination studies in LLMs [22] mainly focusing on factuality hallucination originated from the language knowledge, the hallucination problem in LMMs refers cross-modal inconsistency between the given visual contents and the generated responses for the user instructions. After the seminal works [13, 42, 68] giving eyes to LLMs to understand visual contents with visual instruction tuning, numerous cutting-edge LMMs [61, 9, 41, 36] actively have been proposed. Albeit the scaling laws following more stronger versatile vision models [37, 29, 33] , higher resolution [41, 8] , deeper alignment layers [5, 45, 8] , larger model sizes, etc., LMMs still suffer from generating responses that seem plausible but are factually incorrect for the given visual contents. What is happening to the dog's face in this photo?Figure 1 : The overall decoding procedure of CODE. After LMMs generate detailed description for the visual content by themselves (see instruction in Appendix. A), the model recursively outputs logits from each v and d. By contrasting between two log-likelihoods, CODE produces more contextual and correct responses that match the given visual content suppressing inconsistent words (catching→hit).The origin of LMM hallucination is an intertwined problem for their inherent training paradigm, which involves alignment projection matching during the pre-training, followed by fine-tuning with the limited instruction-following data. Several approaches, aimed for mitigating hallucinatory effects, have addressed the inconsistent issues in the context of data-associated solution [39, 56] , scaling model architectures [65] , or additional RL-based training [64, 52] . Among them, reactive methods [23, 14, 26] intervene the decoding phase of LMMs' inference and alleviate undesired responses. Motivated by Li et al. [34] that have proposed contrastive decoding (CD) method between expert and amateur language models, recent CD-based approaches in LMMs have proposed several ways of contrasting model responses from visual inputs with their counterparts (e.g., visual contamination [30] , image-biased models [69] , or fine-grained visual information [10] ).Our research question begins with "How effectively do contemporary LMMs capture visual evidences in their descriptive responses, and what information must be curbed to produce informative and consistent responses?". As illustrated in , when asking LMMs to generate a comprehensive description for visual content, the output seemingly generates detailed description effectively, but a closer examination often reveals missed fined-grained information or hallucinatory instances in the responses. By recursively referring these incomplete descriptions generated by the models themselves, we aim to restrict the incorrect information flow during the generation phase and enhance the alignment of the model responses grounded in true visual evidences.In this paper, we introduce a novel training-free contrastive decoding method, COuntering DEscription Contrastvie Decoding (CODE), designed to use self-generated descriptions as a contrastive reference to mitigate hallucination issues in LMMs. The core idea of our proposed method is on harnessing the self-generated descriptions which possibly encompass both factual evidence and hallucinatory information from visual contents as a look-up reference for response correction. Specifically, within our contrastive framework as illustrated in Fig. 1 , the comprehensive descriptions from model itself alternatively propagate to visual input tokens and contrast the discrepancy with the logits from actual visual contents to enhance next-token prediction. In addition, we introduce a dynamic restriction strategy that enables adaptive control of information flow during the auto-regressive decoding phase, taking into account both token-level predictions and their distribution within the vocabulary set.By conducting extensive experiments and analyses on prevailing cutting-edge LMMs [40, 51, 16, 41, 62, 8] , we corroborate the effectiveness of our method in reducing hallucination and enhancing the coherence and informativeness in various benchmarks [35, 42, 52, 53, 57] . Our decoding method can be seamlessly integrated into existing LMMs by simply substituting the image tokens with self-generated descriptions in a training-free manner.Our contribution can be summarized into three-fold as follows:• We introduce COuntering DEscription Contrastive Decoding (CODE), a training-free decoding strategy that employs self-generated descriptions to minimize hallucinations in LMMs.By contrasting logit information from descriptions with actual visual contents, CODE enhances visual consistency and coherence in the model responses. • Our approach incorporates dynamic restriction strategies within the contrastive decoding phase. It selectively regulates the information flow by adjusting token-level predictions based on their distribution in the vocabulary, thus ensuring more contextual responses. • We validate the effectiveness of our decoding method across various benchmarks using cutting-edge LMMs. The results demonstrate that CODE significantly reduces hallucination while enhancing the relevance and informativeness in the responses. 
related work: 
 Vision+LLM: Large Multi-modal Models After the emergence of large-scaled LLMs [4, 54, 11] that can interact with users with question-answer chat, various of vision+LLM studies-i.e., LMMs, have been proposed to integrate the robust linguistic capability into the visual understanding and reasoning in diverse vision-language task. As earlier works such as LLaVA [42] , Instruct-BLIP [13] , and MiniGPT-4 [68] , which utilized visual instruction tuning, have bridged two modalities of vision and language through fine-tuning with a learnable query, exemplified by Q-Former [31] or projection layer-based alignments [40] . To enhance cross-modal consistency in vision-language representation, recent works have proposed several solutions to address the underlying weaknesses in both modalities: (i) utilizing higher-resolution visual inputs [41, 9] , (ii) deploying Mixture-of-Expert (MoE) concepts integrating versatile vision models [37, 29, 33] , (iii) improving weak alignment interface [5, 45, 8] , or (iv) adopting larger LLMs to scale up the language model prior [62] .Hallucination Issue, Harming Cross-modal Consistency Despite of the endeavor developments of LMMs, they cannot be free from cross-modal inconsistency between the visual contents and their generated responses, so-called hallucination [49] . This not only leads to performance degradation but also provokes an over-reliance issue, resulting in incorrect model responses that are not grounded in true visual evidence. This critical concern regarding response trustworthiness and model reliability hinders the adoption of LMMs in real-world applications. To mitigate the hallucination problem, diverse works have been proposed employing additional training on curated datasets [52, 55] or reinforcement learning under feedback systems [64, 66] . Among them, by intervening during the response generation, decoding-based approaches [34, 12] are introduced to encourage models to represent more precise responses. We refer to readers for more comprehensive survey papers [43, 3] addressing hallucination in LMMs. Our work is in line with CD-based approaches that utilize logit discrepancy from counterpart outputs to enhance coherence. Unlike the previous works [30, 23, 10] that focus on twisting visual information, we utilize self-generated description as contrasting visual counterpart and correct hallucinatory responses based on the model understanding.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: We include discussion section for potential limitation of our work.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
proposed method: 
 Problem Setup and Preliminaries. Let M θ denote a vision-language LMM parameterized by θ that auto-regressively generates responses for the given visual contents v and input textual query x.Then the model maps the logit distribution to the next token prediction output y t ∈ R |V| at time step t in the vocabulary set V such that y t ∼ p θ (y t |v, x, y <t ) ∝ logit θ (y t |v, x, y <t ), where y <t indicates all previously generated tokens. During the response generation, we can deploy several decoding strategies to choose the next word using either deterministic search (e.g., greedy, beam search) or stochastic sampling (e.g., top-k, Nucleus search [20] ).After the seminal works [34, 12] in natural language processing have introduced Contrastive Decoding (CD) mechanism, which considering information disparities between expert and amateur models for more coherence and informativeness, various works have deployed this strategy into LMMs by twisting visual contents [30] or model information [69] for the contrastive approach. The next-token probability p cd from CD can be generally formulated as follows:EQUATIONwhere v and θ indicates visual counterparts and sub-optimal amateur model, respectively-note that θ= θ can be regarded as self-correction. Intuitively, the objective of CD is amplifying model outputs by reflecting information deviation between top candidate log-probabilities. Therefore, the selection of logit counterparts for referring is the key challenge for high-quality and consistent responses during the contrastive decoding frameworks.As the visual counterpart, we deploy comprehensive image descriptions generated from the model as contrasting reference, which are inevitably less informative than the visual contents themselves. Our motivation is on the innate difference of information density between vision and language [19] . While vision information exhibits relatively less redundancy for spatial signals-e.g., human can visually recognize objects with a few masked patches on images, languages contain high-entropy information, resulting in spans that are more semantic and information-dense than visual signals. That is, it is difficult to infer blanked-out words-e.g., "I went to the store to buy some ____". Building upon the property of each modality, we first delve into the self-generated model responses with specific instruction for the visual contents to elicit the encompassed visual evidences in the representation space and assess its potential subject role as a visual counterpart for contrastive decoding. As illustrated in Fig. 1 , we input a query instruction into the model to generate a comprehensive visual description for the given visual content (please see the detailed instruction x 0 for the self-generated description in Appendix. A). Then, we exploit the generated description as recursive visual inputs, replacing the position of image tokens in the model input sequence (e.g., <image> token in LLaVA series [42, 40, 41] ). Ideally, if the model sufficiently covers whole visual evidences to answer any vision-related questions, the generated response should provide competent answers with solely utilizing the description-only embeddings as an alternative of visual embeddings. However, as shown in Fig 2 , it is obvious that the results from description-only show sub-optimal performance to answer questions due to insufficient information in capturing visual evidences. Consequently, we use the comprehensive description for the visual contents, which is generated by model itself-but partially incorrect or hallucinatory to capture visual evidences, as a contrasting visual counterpart to enhance response coherence during the decoding phase, which also in line with the amateur model selection philosophy of contrastive decoding [34] .Based on our analysis in sec. 3.1, we can obtain a pair of the visual content and its comprehensive description (v, d), such that d corresponds to M θ (y|v, x 0 ). By contrasting the logit variation between the paired information into the model response generation, we can formulate the next-word prediction using our proposed method, COuntering DEscription Contrastive Decoding (CODE):EQUATIONHere, unlike the previous approaches [34, 30, 12] that restrict the logit variations with fixed α value as in Eqn. 1, we present a dynamic restriction α t for the logit variations by comparing the information between visual contents and their comprehensive descriptions. Revisiting the role of α, it determines whether to promote or curb information from logit variation, thus directly influencing next-token generation-higher value results in more aggressive adjustment for the variations. However, when confronting that both v and d yield similar logit score on the correct token, the variation gets closer to zero, thereby the next-token prediction can be unexpectedly reversed if other tokens get rewarded than the correct token with a fixed α on a token-by-token basis. Although this aligns with the initial intent of CD, a more robust selector is necessary to effectively restrict the logit information flow.Accordingly, our method predicts next-token not only at the individual token-level but also considering its distribution across the entire vocabulary set, enabling dynamic control of the information flow.To measure the relative entropy between the token distributions from visual contents P v t and its comprehensive description P d t at time step t, we deploy Bounded Divergence (D bd ) [6] , which is a type of statistical distance that ensures symmetric and bounded measure:EQUATIONwhere D bd (P ∥Q) ≥ 0 and equals 0, if and only if p=q, and k denotes a smoothing parameter. Here, the upper-bound of the divergence apparently exists, such that D bd (P ∥Q) ≤ n i=1 p i log 2 2=1, due to the following condition |p i -q i | ≤ 1.We define the dynamic restriction α t as 1-D bd (P v t ∥P d t ), where it enables a token-wise feedback control that adjusts the information weighting with respect to the closeness of the two distributions. The major role of the restriction term is maintaining a balance in the logit variation for the observed prediction disparities between the v and d distributions. That is, when the distributions are close enough (i.e., P v t ≈ P d t ), the value of D bd (P v t ∥P d t ) approaches zero, indicating minimal divergence. That is, α t approaches 1, allowing for higher amplification of logit variations in predicting the next-token outputs. This adjustment reflects the increased reliability of predictions when the two distributions from v and d are closely aligned. On the other hand, for the dissimilar distributions, α t decreases towards zero, compelling the model to restrict information flow from the variation. This reduction limits the potential for introducing erroneous or less probable predictions by focusing more on visual information, thereby maintaining coherence in the output when the model's understanding of the visual content significantly deviates from its textual description.One major challenge in contrastive-based decoding is the scenario where implausible tokens are rewarded, even when predictions are made with low confidence. This issue can also arise in our method, particularly when token distributions derived from textual descriptions provide more confidence than the visual content, ironically undermining the most predictive tokens. To address it, Li et al. [34] have introduced an adaptive plausibility constraint, which filters out less plausible tokens by truncating them based on the maximum token confidence from the expert model. While this approach simply penalizes false positive tokens in the candidate pool, it may also have unintended side effects by prematurely applying a cutoff threshold to lower-confidence tokens. Specifically, early threshold settings can sometimes eliminate the possibility of identifying correct token predictions, which might otherwise be dismissed in a pool considered to contain mostly false negatives.Improving the previous constraint [34, 30] , we present adaptive information constraint (V head ) designed to dynamically retain tokens that may be informative despite their lower confidence. By comparing prediction distributions between P v t and P d t , we filter out less relevant tokens from the candidate pool as follows:EQUATIONwhere β t dynamically regulate the token candidate pool utilizing the divergence term in Eqn. 3, defined asβ t = D bd (P v t ∥P d t ). This strategy can expand the token searching pool when the nexttoken prediction, derived from both visual content and comprehensive description, shows a similar distribution yet uncertainty in selecting the candidate token (i.e., false negatives). Finally, we only consider the next-token prediction within V head (y <t ), and for the tokens satisfying y t / ∈ V head (y <t ), we set their logits to -∞ to filter out from the candidate pool. Please see comprehensive Algorithm. 1 in Appendix. B.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 10. Broader Impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [Yes] Justification: We add it in discussion section. Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
experiments: 
 Experimental Setup To validate the efficacy of our method over various LMM families and sizes, we implemented our method on contemporary LMMs: LLaVA-1.5 (13B) [40] , Emu2-Chat (14B) [51] , InternLM-XComposer2 (7B) [16] , LLaVA-NeXT (34B) [41] , Yi-VL (34B) [62] , and InternVL 1.5 (26B) [8] . We compared our method with five baseline decoding strategies. For the regular decoding strategies, we used greedy decoding, Nucleus sampling [20] , and beam search decoding. Additionally, we selected OPERA [23] and VCD [30] for contrastive decoding method, which designed to mitigate LLaVA-1.5 [40] Emu2-Chat [51] IXC2-VL [16] LLaVA-NeXT [41] Yi-VL [62] InternVL 1.5 [8] Figure 3 : Overview of experimental results on 6 baseline LMMs, 6 decoding method, and 6 hallucination benchmarks in spider chart format.hallucinations with contrastive frameworks. We used the default parameter settings for all methods, where top-p value 0.95 and temperature 1.0 for Nucleus sampling, the number of window size for searching is 5 (i.e., num-beams 5) for both beam search decoding and OPERA, and CD-α = 1, CD-β = 0.1 for VCD, and k = 0.3 for our method. Note that OPERA inference requires too much memory especially for LLaVA-NeXT (34B), so that we excluded OPERA results for this model.The benchmarks for evaluating hallucinations in LMMs can be broadly categorized into discriminative and generative streams. The discriminative type assesses hallucinations by evaluating the predicted answer among given options (e.g., multiple choice or yes/no question), while generative benchmarks typically employ more advanced language models (e.g., GPT-aided evaluation) to rate the subject model descriptions. Within this taxonomy, we carefully select 6 benchmarks to test baselines. Please see Appendix. C for benchmark details.As discriminative benchmarks, we utilize mainly three datasets for detailed evaluation. Specifically, POPE [35] is a commonly used benchmark for detecting object hallucination by converting object annotations sourced from MSCOCO [7] . Under the three different subsets: random, popular, and adversarial, the metric for POPE measures binary classification performance for simple yes/no questions. MMVP [53] aims to evaluate the understanding of visual details for 9 different visual patterns using paired classification accuracy. Due to its evaluation design, which involves comparing two similar CLIP-blind image pairs, MMVP requires LMMs to capture subtle visual differences.RealworldQA [57] is the most recent dataset tailored to assess the capability of LMMs in basic real-world spatial understanding, using the accuracy metric within multiple-choice questions.We use three benchmarks for generative benchmarks, extending the evaluation scope to include openended captioning tasks beyond merely assessing classification within given answer options. Generally, ChatGPT [46] is used to score the quality of the model-generated sentences. The metric for both LLaVA-QA90 [42] and LLaVA-Bench (In-the-Wild) [42] is score ratio, where model responses rated from GPT-4 [47] are divided by GPT-4 answers such that |model-score|/ |GT-score|, where all scores are rated by GPT-4. It has three types of questions: conversation, detailed description, and complex reasoning. MMHal-Bench [52] evaluates the degree of hallucination for the 8 various question types: object attribute, adversarial object, comparison, counting, spatial relation, Table 1 : The hallucination evaluation results for the discriminative benchmarks [35, 53] environment, holistic description, and others. GPT-4 measures the severity of hallucination in a range of 0 to 7 and the higher score denotes less hallucination.Overview We summarize our comprehensive experimental results across six LMMs, six decoding methods, on six benchmarks in a spider chart format for visibility of the improvements in Fig. 3 . As in the figure, CODE generally shows competent performance and consistent results among different measurements and benchmarks. We delve into each result in detail in the subsections below.We conduct hallucination evaluation across 6 LMM baselines on two discriminative benchmarks. POPE [35] focuses on object-level hallucinations, where its prompts consist of asking about the existence of objects, such as "Is there <something> in the image?". Here, we exclude trivial splits and test with the most challenging subset: adversarial split, which requires models to identify highly-relevant objects to correctly answer yes/no. As shown in Table 1 , both the accuracy and F1 score from CODE lead to the best performance in 6 LLM baselines, among 6 decoding methods, which indicates that our decoding method can properly address object hallucination within simple binary question format.As a more sophisticated visual assessment, we compare our method on MMVP benchmark [53] .Beyond object hallucination, the benchmark is divided into 9 systematic visual patterns within CLIPblind pairs. These pairs consist of two images that clearly display visual differences, yet CLIP [48] struggles to discriminate between them. The test subject models only receive scores when they correctly identify both images in the pair, requiring precise visual understanding. On the right part of Table . 1, we can observe a remarkable gain in average, indicating that CODE effectively enhances visual consistency through the contrastive mechanism using self-generated descriptions.Results on GPT-aided Benchmarks Although the discriminative datasets are intuitive benchmarks for evaluating the degree of object-level hallucination, they are limited in measuring a deeper under- [42] , showing up to +13.7% improvements than other CD methods. Additionally, we compare our models in MMHal-Bench [52] specialized to evaluate hallucination effects sourced from more challenging image-question pairs-OpenImages [50] . As in the result, our method generally not only improves overall average score with consistent results among 6 other baseline LMMs, but also effectively mitigates the hallucination ratio. Through combinatorial results from both discriminative and generative benchmarks, we corroborate the robustness and efficacy of our proposed method in addressing both object-level and contextual hallucinations, thereby ensuring more reliable LMM responses. The brand of the blueberry-flavored yogurt is Fage.Question: What is the brand of the yogurt flavored with blueberry? (a) RealworldQA [57] (b) LLaVA-Bench (In-the-Wild) [42] Figure 5 : Additional experiments on In-the-Wild benchmarks. Note that, unlike other datasets, OPERA [23] fails to generate consistent responses in real-world datasets using Yi-VL [62] . Computational Analysis We utilize comprehensive descriptions from models as additional information for contrasting with visual contents, thereby leading to computational loads similar to other decoding methods. To analyze the computation, we compare the token throughput (token/s) and decoding latency (ms/token) with other CD-based methods on 8 NVIDIA RTX A6000 GPUs as in Table . 4.As illustrated in Fig. 4 , to verify whether the proposed CODE effectively mitigates object hallucination, we analyze the output logit values of LMM [41] at the token-level case study, with a greedy search as the baseline. The first two rows in the table indicate the original greedy decoded tokens which are elected based on high logit v from the visual content and CODE output tokens, respectively. As in the figure, we can observe that visual hallucination occurs at the "Yoplait" token highlighted in red. For relatively easy tokens at the beginning of sentence, logit code produces identical decisions maintaining consistency with logit v , which indicates the amplification of logit variation is effectively adjusted due to similar prediction distributions from visual contents and description-only information. However, at the hallucination-occurred time step, logit scores are deviated between the two information, resulting in a more confusing state to identify between GT token "Fage" and hallucinatory "Yoplait". In our framework, "Fage" is relatively more amplified from 15.02 to 16.66 than "Yoplait", which changes from 15.34 to 15.30. By simultaneously considering both token-level and distributional prediction over the vocabulary, CODE changes the wrong nexttoken output to correct one, mitigating hallucination. For more case studies, please refer Appendix. D.Contemporary open-sourced LMMs are fine-tuned with various combinations of vision-language datasets [7, 28, 25] , mostly composed of COCO-sourced visual images [38] and their curated instruction. Although the existing hallucination benchmarks intentionally convert question queries to assess model robustness against inconsistency, the visual contents in benchmarks are limited to in-distribution COCO images. To validate our method in more challenging and real-world scenarios, we compared baselines on LLaVA-Bench (In-the-wild) [42] and RealworldQA [57] as in Fig. 5 and achieved competent performance (case studies in Appendix. F). 
discussion and limitation: 
 Albeit the computational analysis in Table . 4, as one of limitations, our contrastive decoding method requires additional computational resources than the use of vanilla decoding. However, considering an essential ongoing research topics and developments [60, 15] aimed at mitigating the negative effects of hallucination problems in both LLMs and LMMs, our work contributes important societal impacts towards more real-world applicability and robust AI system. 
conclusion: 
 We present COuntering DEscription Contrastive Decoding (CODE), a novel and training-free decoding method to mitigate hallucination in Large Multi-modal Models. By utilizing self-generated descriptions as corrective references during the decoding phase, CODE dynamically adjusts the information flow for next-token predictions, enhancing the coherence and informativeness of responses while reducing the cross-modal inconsistency. Extensive experiments demonstrate that CODE effectively decreases hallucinations across various benchmarks and contemporary LMMs, significantly improving contextual relevance and response alignment with visual contents. 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [NA] Justification: This is not relevant with our work. Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [NA] Justification: Our work do not include any license assets. Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. • If this information is not available online, the authors are encouraged to reach out to the asset's creators. 
new assets: 
 Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets? 
 
 Appendix: In generating a comprehensive description for the given visual content, we aim to obtain as detailed a description as possible, ensuring that the response fully spans the visual representation space, even though it may not be entirely feasible as discussed in 3.1. The specific prompt instruction used to describe the image contents in detail is described in Table . 5.Provide a detailed description of the image, covering all visible elements and their interactions, so as to thoroughly answer any potential questions about the image. p v = Softmax [logit θ (y t | v, x, y <t )] ▷ Compute p v 5: p d = Softmax [logit θ (y t | d, x, y <t )] ▷ Compute p d 6: D bd (p v ∥p d ) = 1 2 n i=1 (p v,i + p d,i ) log 2 (|p v,i -p d,i | k + 1) ▷ Bounded Divergence D bd 7: Set α t ← 1-D bd (p v,• POPE [35] is a widely used benchmark designed for evaluating object-level hallucination, which can be split into the three subset categories based on how to select object replacements: (i) random, randomly sampled objects (ii) popular, top-k frequent objects not existing in the image, and (iii) adversarial, top-k objects that have high co-occurrence. The number of images is 500 and each image has 6 questions along with the subsets, making a total of 9000 images. In this work, we only consider adversarial split, which is the most challenging subset in POPE benchmark. • MMVP [53] includes 300 images with 9 different visual patterns that CLIP model [48] struggles to identify the visual differences (CLIP-paired images): Orientation and Direction (☼), Presence of Specific Features ( ), State and Condition ( ), Quantity and Count ( ), Positional and Relational Context ( ), Color and Appearance ( ), Structural and Physical Characteristics ( ), Text ( ), Viewpoint and Perspective ( ). It follows multiple selection tests, but uses GPT-4 to map the model response to the answer options. • RealworldQA [57] is recently introduced benchmarks for evaluating basic real-world understanding for multi-modal models. It consists of total 765 anonymized outdoor (mostly taken from vehicles) and indoor images with multiple selection questions.Figure 6 : Additional token-level case study for analysis.In another example illustrated in Fig. 7 , we show how the adaptive information constraint prevent from rewarding implausible tokens, thus suppress hallucinatory prediction during contrastive decoding. The original tokens from logit v predict the correct answer at the hallucinatory time step, highlighted in bold. In this case, the original prediction should be preserved, and token inversion should not occur. By our CODE decoding method, β t dynamically controls the adaptive information constraint, so that those hallucination tokens'(i,e., "four" and "dragon") logit values are cut off to -∞ and removed from candidate token pool.We proposed CODE, which can be seamlessly integrated into LMMs without additional training.There is still a lot of room for progress and mitigation of hallucination issues, as our method cannot assure 100% removal of hallucinations. However, by providing more coherent and contextually accurate responses, our work can potentially be integrated into real-world applications, making user interactions with AI in customer service, education, and personal assistance more effective and satisfying in the near future.Furthermore, enhanced accuracy and reliability using our method can reduce hallucinations in LMMs, improving the accuracy of AI-generated descriptions in critical fields such as autonomous GT Answer: There are three uncut fruits in the image.Question: How many uncut fruits are in the image? driving, robotics, healthcare, and augmented reality. This advancement not only enhances practical applications but also significantly benefits the research community working on hallucination, an area that is not yet fully explored. Our contributions can help pave the way for deeper understanding and new research directions to address these challenges, for more trustworthy AI systems.Discriminative Capability Case Study: MMVP.Image: Question: Is the following statement correct: In the image, there is one daisy that is under the shadow of a taller daisy. InternLM-XComposer2 (Opera):The answer is (a).The answer is (a).The answer is (b).In the image, is the snake's head oriented facing upwards or directed forwards? The snake's head is oriented (b) forward.The snake's head is directed forwards, so the correct answer is (b) Forward.The snake's head is oriented facing upwards, so the correct answer is (a) Upwards. The needle of the syringe is pointing down.The needle in the image is pointing up.Image: Question: Based on the image, is the following statement correct: We cannot see the window on the school bus? LLaVA-NeXT (Ours):The statement "We cannot see the window on the school bus" is (a) Correct. In the image provided, there is no visible window on the school bus. The view is focused on the back of the bus where the stop sign and the words "SCHOOL BUS" are visible, but the window is not shown.Table 6 : More case studies within MMVP, using InternLM-XComposer2, InternVL 1.5 and LLaVA-NeXT.Generative Capability Case Study: LLaVA-Bench (In-the-Wild).Question: Describe this photo in detail.Image: InternLM-XComposer2 (Opera):The painting you're referring to is the famous "Mona Lisa" by Leonardo da Vinci. It is an oil painting on a poplar wood panel.The artwork depicts a woman, believed to be Lisa Gherardini, the wife of a Florentine merchant named Francesco del Giocondo. The woman is portrayed in a three-quarter view, with her left hand resting on her lap and her right hand holding a veil. She is wearing a dress with a high collar and a pearl necklace. The background features a landscape with mountains and a body of water. The painting is known for its enigmatic smile and the use of sfumato, a painting technique that creates a soft, hazy effect. The "Mona Lisa" is considered a masterpiece of the Italian Renaissance and is housed in the Louvre Museum in Paris, France.The image features a painting of the renowned Mona Lisa, an oil on wood artwork. The central figure is a woman with long red hair and a headband, wearing a blue dress adorned with gold accents. Her face is captured in close-up detail, showcasing her distinct smile and enigmatic gaze. The background is a landscape painted in earth tones, adding depth to the composition. This painting is widely recognized as a masterpiece, capturing the essence of the Renaissance era with its realistic portrayal and intricate details.The painting you're referring to is the famous "Mona Lisa" by Leonardo da Vinci. It is an oil painting on a wooden panel, measuring approximately 77 x 53 centimeters. The painting is a portrait of a woman, who is the focal point of the artwork. She is depicted wearing a dress and a headscarf, with her hair styled in a manner typical of the Italian Renaissance period. The background of the painting features a landscape with mountains and a body of water, adding depth and context to the scene. The painting is known for its realistic portrayal of the woman's features, her enigmatic smile, and the subtle interplay of light and shadow. The "Mona Lisa" is considered a masterpiece of the High Renaissance period and is housed in the Louvre Museum in Paris, France.Image: InternVL 1.5 (Opera):The image shows a group of star fruit, also known as carambola or tamarind, arranged and presented. These are a kind a of sweet, fizzi, and tart tasting, and are a popular fruit. There appears a few different ways they're displayed, some whole or unpeel, some are half-cut open. There is a clear visual focus and contrast, as well as a natural and organic feel to the image.This image depicts several purple-colored fruits, which appear to be durian. Durian is a tropical fruit that is known for its strong, pungent aroma and creamy, sweet flesh. In the center of the image, one of the durians is cut in half, revealing the fruit's inner flesh, which is a creamy white with a star-like pattern of seeds.The cut side of the durian shows the texture of the flesh and the arrangement of the seeds within.The fruit has a distinctive appearance with a thick, spiky green outer shell that protects the flesh inside. The surface of the fruit is textured and somewhat rough, with the color transitioning from deep purple to green at the points where the spiky projections emerge. The durians are resting on a wooden surface, which provides a rustic backdrop that contrasts with the vibrant colors of the fruit. In the background, there is a wicker basket containing more of these fruits, suggesting that they have been gathered for consumption or sale. The basket is made of natural materials and complements the overall natural, organic feel of the image. The lighting in the photo is soft and natural, which highlights the textures and colors of the durians and adds to the overall appeal of the image. Overall, this image presents the durian fruit in a way that showcases both its visual and textural qualities, as well as its role in a natural, possibly agricultural, setting.The image shows several whole and cut open mangosteens on a wooden surface. Mangosteens are tropical fruits known for their distinctive appearance and sweet, slightly acidic flavor. The fruit has a dark purple, almost black, outer skin that contrasts with the green, bulbous protrusions at the top, which are the remnants of the fruit's calyx. One of the mangosteens is cut in half, revealing the white, segmented flesh inside, which is the edible part of the fruit. The segments are arranged in a flower-like pattern around a central seed pod. The cut-open fruit provides a clear view of the fruit's structure and the contrast between the outer skin and the inner flesh. The wooden surface and the natural lighting give the image a rustic and organic feel, emphasizing the freshness of the fruit.Question: Describe this photo in detail.LLaVA-NeXT (VCD):In the center of the image, a black bowl with vibrant red and yellow trim commands attention. It cradles a hearty serving of noodles, their creamy yellow hue contrasting with the bowl's dark color. A few pieces of meat, their rich brown tones adding to the variety of colors, are scattered throughout the bowl. Green peas peek out from under the noodles, adding a touch of freshness to the dish. To the left of the bowl, a small white cup filled with a green sauce waits patiently. Its color is a soothing contrast to the bold colors of the bowl and its contents. In the top right corner of the image, a white plate holds a small pile of chopped vegetables. Their bright green color stands out against the plate, drawing the eye to the top right corner of the image. The arrangement of these objects creates a visually pleasing scene. The bowl of noodles is centrally placed, making it the focal point of the image. The green sauce in the white cup and the plate of chopped vegetables are positioned on either side, framing the bowl and creating a balanced composition. The colors, objects, and their relative positions all contribute to a harmonious and appetizing scene.LLaVA-NeXT (Ours):In the heart of the image, a black bowl with a vibrant red and yellow border takes center stage. It cradles a hearty serving of udon noodles, their light brown hue contrasting beautifully with the bowl's dark exterior. The noodles are not alone; they are accompanied by a generous portion of green onions, adding a pop of color and hinting at the freshness of the dish.To the right of the bowl, a white cup filled with a green sauce waits patiently, ready to enhance the flavors of the udon noodles. The cup's pristine white color provides a stark contrast to the green sauce within, making it an eye-catching element in the composition.In the background, a blue and white plate holds a serving of meat and green onions. The plate's colors echo those of the bowl, creating a harmonious color palette across the image. Scattered around these main elements are various condiments and utensils, each playing their part in this culinary tableau. Their presence adds depth to the image, suggesting a meal ready to be enjoyed. Overall, this image captures a moment of anticipation, just before the enjoyment of a delicious meal begins.Table 8 : More case studies within LLaVA-Bench (In-the-Wild), using InternLM-XComposer2, InternVL 1.5 and LLaVA-NeXT. We marked the expressions where hallucinations occurred in red.As discussed in Appendix. E, even if CODE shows competent performance along various benchmarks and LMM baselines. The hallucination cannot be 100% eliminated. In this section, we attached some of failure cases to shed some light for future work direction. As illustrated in Table . 8, the baseline models fail to correct hallucination even with our CODE method. Upon closer examination of the model responses, it is evident that the hallucinatory responses tend to be biased towards language priors such as "strawberry-flavored" or "holding a glass of beer". Our approach mainly uses self-generated description as contrasting reference (i.e., close to the concept of self-correction), thus the strong assumption is on that the amateur model (comprehensive description) should generate not too much deviated responses from true answers.In the failure examples, we can infer that the reliance on self-generated descriptions may not always suffice, especially when the descriptions themselves are biased or inaccurate. This indicates a need for integrating more robust mechanisms to verify and correct these biases. Additionally, enhancing the model's understanding and processing of visual content could help mitigate such issues. Future work could explore the integration of external knowledge sources and more sophisticated bias detection techniques to further reduce hallucinations and improve the overall accuracy and reliability of LMMs.Image:Question: Is there any strawberry-flavored yogurt in the fridge?The image showcases the inside of a refrigerator, filled with various food items. On the top shelf, there is a carton of milk and a container of yogurt. The middle shelf contains a bag of carrots, a package of strawberries, and a blue plastic container. The bottom shelf holds a carton of blueberries and another carton of yogurt. Additionally, there are two bottles on the door of the refrigerator.Yes, there is a container of strawberryflavored yogurt in the fridge.Question: What is the man doing in the image?The image depicts a group of people gathered in a room, with some sitting and others standing. There are at least 12 people in the scene, engaged in various activities. One man is sitting at a table, holding a cell phone, while another man is sitting nearby, giving a thumbs-up gesture. There are several chairs in the room, with one near the center and another towards the right side. A dining table is also visible in the scene, with a cup placed on it. The people in the room appear to be enjoying their time together, possibly at a social event or gathering.The man in the image is sitting at a table, holding a glass of beer, and giving a thumbs-up sign. Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 


Decision: This paper proposed a contrast decoding method called CODE for handling hallucinations of large multi-modal models. It is interesting to use descriptions for enriching language information to address hallucination problems. The idea of contrast image and caption in the VLM decoding process is new, and the proposed method is quite effective on multiple benchmarks. Due to these merits, most of the reviewers are positive about this paper. Though one of the reviewer still has concerns about the details of experiments, authors have addressed them during rebuttal. Thus, AC recommends acceptance.

Reviews: review: paper_summary: paper_summary: Large Multi-modal Models (LMMs) have made significant strides in understanding visual context and generating coherent responses. However, they face challenges such as hallucinations, where responses are incorrect and unrelated to visual inputs. To tackle this issue, this paper proposes COuntering DEscription Contrastive Decoding (CODE). CODE uses self-generated descriptions as reference points during decoding to mitigate hallucinations. By aligning responses with visual content through dynamic adjustments in token predictions, CODE enhances coherence and informativeness without requiring additional training. Experimental results demonstrate CODE's effectiveness in reducing hallucinations and improving cross-modal consistency across various benchmarks and state-of-the-art LMMs.
strength_weakness: strength_weakness: 1. From Table 1, it can be seen that CODE does not bring much performance improvement compared to greedy decoding in practice.

2. Did the authors consider evaluating with the CHAIR metric on generative tasks?

3. Can CODE be used in conjunction with VCD or OPERA?
questions: questions: see weaknesses.
limitations: limitations: Yes
paper_summary: paper_summary: The paper proposed a contrast decoding method named CODE for large multi-modal models. CODE, as mentioned by its name uses self-generated description as contrasting references during the decoding phase of LMMs to mitigate the hallucination issues. CODE works by dynamically considering the variations between the visual features and their corresponding pure language features (description) to improve response alignment with actual visual content and misalignment with the wrong part in descriptions. Based on contrastive decoding, the author proposed dynamic restriction, which regulates the information flow, and adaptive information constraint to filters out less plausible tokens constraint in the contrastive decoding phase. The proposed method is verified on 6 benchmarks.
strength_weakness: strength_weakness: 1. The method has to generate a comprehensive description for each image before doing the visual question answering, the inference time is long and not convenient.
2. Only conduct ablation study of DC and AIC on MMVP and LLaVA QA 90, lack of ablation study on other benchmarks, such as, POPE.
3. Lack of analysis: why the performance of proposed method is not optimal or even inferior to the underlying Greedy decoding on some problem types of MMVP. For example, Color and Appearance in MMVP.
questions: questions: 1. Have the authors tried other divergence measures such as KL divergence for the Bounded Divergence used in CODE?
2. For the computational analysis in Table 4, have you considered the cost of generating the comprehensive descriptions?
3. A question about the DC, I don't understand, when $\mathcal{D}_{\mathrm{bd}}\left(P_t^v \| P_t^d\right)$, which means there is little difference between decoding based on visual features and decoding based on pure language features. It is intuitive to decode without regard to the variations, but why does the CODE highly consider the variation?
limitations: limitations: As mentioned in the first point of Weakness.
paper_summary: paper_summary: This paper introduce the CODE decoding method to contrast origin image with VLM-generated image description to reveal the missed or hallucinated content in naive decoding process. The method contains two innovations: 1) a Bounded Divergence guided selector to provide dynamic combining weight. 2) an adaptive information constrain based also on Bounded Divergence. The experiment results show promising and consistent performance improvement.
strength_weakness: strength_weakness: 1. The dynamic information flow control does not have that much technical novelty, as it is quite close to what IBD does.
2. Like CODE, VCD also contrast image with modified inputs (i.e. corrupted images), but not discussed in detail in this paper. What makes contrasting image descriptions better than contrasting with corrupted images?
3. What makes a good image description for CODE to use is not discussed.
questions: questions: See weaknesses.
limitations: limitations: the authors adequately addressed the limitations

Comments: comment: Dear reviewers,

As the discussion period will end soon, we kindly ask that you review our additional responses, in which we have made every effort to comprehensively address your remaining questions. 

If you have any further concerns, we would greatly appreciate it if you could share them with us, and we would be happy to discuss them with you.
We sincerely appreciate for the acitive discussion and responses for the reviewers during the discussion period. We have carefully revisited additional questions and addressed them comprehensively. We respectfully ask reviewers to review our responses, also happy to address any remaining concerns. We are eager to integrate all your feedbacks into our final version.
Thank you for the detailed response, it addresses my concerns on some details of the method. I tend to hold the score.
We would like to appreciate to the reviewer for the active engagement and discussion. We address each question separately below.

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**A1.** As the reviewer asked, we conducted additional experiments on two in-the-wild benchmarks (RealW-QA and LLaVA(W)) and reported greedy decoding performance in the table below. Note that these results slightly underperform compared to other baseline decoding methods.

|          |  LV1.5 | LV1.5 | IXC-VL | IXC-VL | IVL-1.5 | IVL-1.5 |
|----------|-------:|------:|-------:|-------:|--------:|--------:|
|          | greedy |  ours | greedy |  ours  |  greedy |   ours  |
| RealW-QA      |   51.2  |  56.2 |   56.9  |  62.4  |   63.4   |   66.3  |
| LLaVA(W) |  69.3  |  72.6 |  73.3  |  82.3  |   84.8  |   85.7  |


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**A2.** We acknowledge that the performance gain always cannot be significantly improved, especially considering our extensive experimental comparison across **6 benchmarks and 6 varying size of LMMs**. We will definitely integrate the pointed-out limitation into the final version.

Additionally, we kindly encourage the reviewer to refer to our response for R#FLJA (A3). Based on the additional analysis on MMVP, our CODE method shows more powerful performance especially in the generative tasks that require more comprehensive understanding of the given visual context, compared to the deterministic tasks (such as multiple-choice questions), which are relatively simple and straightforward. We believe that this is attributed to our CODE strategy, which utilizes the comprehensive description from the models themselves as restricting information flow when sequentially generating response tokens, thus showing fewer hallucinatory responses during more longer context generation (beyond a single answering: yes/no or multiple-choice question).
We thank the reviewer for replies and further questions. As the reviewer requested, to investigate the effectiveness of our design choice for the detailed description prompt, we compared it with a different variation, which is a base prompt to obtain a detailed description.

- Base prompt: “Provide a detailed description of the image.”
- Our prompt: “Provide a detailed description of the image, covering all visible elements and their interactions, so as to thoroughly answer any potential questions about the image."

As in the table below, we have analyzed the performance of the mmHalBench and compared the average $\sharp$ token lengths for the responses generated by the models. The results show that the average token length of our curated prompt, designed to include a more comprehensive description of all visible elements in the image, demonstrates a longer token length (approx. 24\% longer) and slightly improved performance compared to the baseline. This indicates that the information amount of the comprehensive description indeed affects to the CODE by reflecting the current visual understanding of LMMs, and effectively mitigates hallucinatory responses. We will include these results and discuss how the prompt designs can boost the CODE performance in the final version.

| Models |       |   LV1.5   |                 |       |  IXC2-VL  |                 |       |  IVL-1.5  |                 |
|----------|:-----:|:---------:|:---------------:|:-----:|:---------:|:---------------:|:-----:|:---------:|:---------------:|
| mmHal    | Overall$\uparrow$ | Hal$\downarrow$ | $\sharp$ token | Overall$\uparrow$ | Hal$\downarrow$ | $\sharp$ token | Overall$\uparrow$ | Hal$\downarrow$ | $\sharp$ token |
| Base |  2.34 |   52.08   |      100.54     |  3.19 |   30.21   |      56.98      |  3.42 |   33.00   |      136.16     |
| Ours     |  2.49 |   51.00   |      115.46     |  3.46 |   25.00   |      80.73      |  3.52 |   30.21   |      195.54     |
Thanks for you repsonses! I still have some concerns.

1. How about the performance of the  greedy decoding in Figure 5？

2. From Table 1 and 2, I think CODE does not always provide much improvement compared with the greedy decoding. It seems that only LLaVA-NeXT and InternVL have a obvious  improvement. I think this is a limitation.
The response addresses my concerns on technical novelty and comparison with baselines. However, I am still curious about the effect of CODE with different image description prompts.