Title: Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation
Venue: NeurIPS 2024
Accepted: yes
Keywords: Vision-Language

Region-Level Understanding

Multimodal Large Language Model
Abstract: The Multi-modal Large Language Model (MLLM) based Referring Expression Generation (REG) task has gained increasing popularity, which aims to generate an unambiguous text description that applies to exactly one object or region in the image by leveraging foundation models. We empirically found that there exists a potential trade-off between the detailedness and the correctness of the descriptions for the referring objects. On the one hand, generating sentences with more details is usually required in order to provide more precise object descriptions. On the other hand, complicated sentences could easily increase the probability of hallucinations. To address this issue, we propose a training-free framework, named ``unleash-then-eliminate'', which first elicits the latent information in the intermediate layers, and then adopts a cycle-consistency-based decoding method to alleviate the production of hallucinations. Furthermore, to reduce the computational load of cycle-consistency-based decoding, we devise a Probing-based Importance Estimation method to statistically estimate the importance weights of intermediate layers within a subset. These importance weights are then incorporated into the decoding process over the entire dataset, intervening in the next token prediction from intermediate layers.
Extensive experiments conducted on the RefCOCOg and PHD benchmarks show that our proposed framework could outperform existing methods on both semantic and hallucination-related metrics. Code will be made available in https://github.com/Glupayy/unleash-eliminate.

Unleashing Region Understanding in Intermediate Layers for MLLM-based Referring Expression Generation

Abstract

The Multi-modal Large Language Model (MLLM) based Referring Expression Generation (REG) task has gained increasing popularity, which aims to generate an unambiguous text description that applies to exactly one object or region in the image by leveraging foundation models. We empirically found that there exists a potential trade-off between the detailedness and the correctness of the descriptions for the referring objects. On the one hand, generating sentences with more details is usually required in order to provide more precise object descriptions. On the other hand, complicated sentences could easily increase the probability of hallucinations. To address this issue, we propose a training-free framework, named as 'unleash-then-eliminate', which first elicits the latent information in the intermediate layers, and then adopts a cycle-consistency-based decoding method to alleviate the production of hallucinations. Furthermore, to reduce the computational load of cycle-consistency-based decoding, we devise a Probing-based Importance Estimation method to statistically estimate the importance weights of intermediate layers within a subset. These importance weights are then incorporated into the decoding process over the entire dataset, intervening in the next token prediction from intermediate layers. Extensive experiments conducted on the RefCOCOg and PHD benchmarks show that our proposed framework could outperform existing methods on both semantic and hallucination-related metrics. Code will be made available in https://github.com/Glupayy/unleash-eliminate .

1 Introduction

Referring expression generation (REG) [11, 29, 31, 53, 54] is a task to generate an unambiguous text description that applies to exactly one appointed object or region in the image. A good expression should be distinguishable enough to ensure that the listener can identify the unique target among various objects within the same image. With the great success achieved by large language models (LLMs), multi-modal large language models (MLLMs) [1, 9, 12, 27, 56, 61] have been introduced to perform this task and become increasingly popular in the research community. Some representative works [6, 37, 52, 55, 57] conduct visual instruction tuning on specialized region-involved multi-modal corpus and successfully empower MLLMs with the ability of region-level understanding.

Though certain progress has been made, MLLMs themselves suffer from object hallucination [14, 23, 46, 50, 60]. Research suggests that the mechanism behind MLLMs' hallucinations could be related to the over-reliance on prior knowledge of LLMs rather than the multi-modal context provided by

output from intermediate layers

Unleash

1. a muffin with chocolate chips

3

MASK

2. the top of a chocolate muffin

in front of 3 other muffins

3. a donut with chips in the foreground

REG

MLLM

1

2

RES

MLLM

Eliminate

Final output:

the top of a chocolate muffin in front of 3 other muffins

Image & Mask

REG

MLLM

RES

MLLM

Cycling in probing set

......

Layer prior importance

1

2

3

4

5

Indices:

REG

MLLM

RES

MLLM

output

Generate without RES

(a) Cycle-consistency-based quality ranking

(b) Probing-based importance estimation

the input [14, 19, 50]. Therefore, MLLM-based REG models naturally inherit the aforementioned hallucination issues. Additionally, a further complication arises in the context of region-level understanding, where models are required to generate precise and identifiable descriptions for specific regions. To achieve that, the MLLMs sometimes have to make use of the surroundings for reference while effectively avoiding out-of-region information distorting the description of the target object, e.g. incorrectly attributing characteristics of other objects to the targeted region (Figure 7 in appendix). This requirement exacerbates the issue of attribute-level hallucinations [3, 50]. In this paper, we aim to explore this potential trade-off between the detailed description and accurate targeting of referring objects in MLLM-based REG task. To be specific, providing precise object descriptions necessitates generating sentences with more details, which results in longer sentences. Meanwhile, the text with increased granularity (length) is more likely to contain inaccurate or spurious information, commonly defined as 'hallucinations.' Table 1 shows an example of quantitative analysis.

To address the trade-off between information richness and reliability, we propose a novel approach called 'unleash-then-eliminate,' as depicted in Figure 1. We observed that the alignment of regionlevel multi-modal information does not maintain a monotonic progression during inter-layer transitions. Moreover, when multi-modal hidden states are projected into the language space using the language head, the intermediate layers sometimes hold more descriptive region information than the final layer. These observations (Section 3.2) imply that the most suitable layer for each referred region should not be solely confined to the final layer. Accordingly, we adopt contrastive decoding [8, 22] to unleash the object information contained in the intermediate layers. To eliminate unsuitable candidate outputs, we leverage the dual task of REG: Referring expression segmentation (RES) [37, 48], which aims to segment a target object mask from the entire image given a sentence describing the object. Ideally, for the same object, the region input (formed as a mask) for the REG task and the mask output from the RES task should exhibit cycle consistency. Based on this insight, we propose a cycle-consistency-based decoding method, which enables us to choose among multiple outputs based on their descriptive quality thus reducing hallucinations while maintaining the richness of the output sentences. Furthermore, considering the need to diminish the computational load of RES, we develop a hybrid layer importance measurement strategy to select the best layer for each token during the next word prediction. This strategy leverages both the layer-wise prior importance estimated over probing subset, and the Jensen-Shannon divergence [8] between the logits of each candidate layer and the last layer. With the layer-wise prior importance, the MLLM-based REG model maintains promising performance in reducing hallucinations and enhancing the granularity of the generated sentences, even without the assistance of RES model. Extensive experiments conducted on the RefCOCOg [33] and PHD [28] benchmark demonstrate that our proposed framework surpasses existing methods on both semantic and hallucination-related metrics.

2 Related works

Region-level understanding in Multimodal Large Language Models. Significant progress has been made in unleashing the region-level understanding ability in MLLMs [6, 18, 37, 52, 55, 57]. To

incorporate region-level information into sequence generation of MLLMs, some approaches [6, 52] integrate bounding box coordinates into the language input in the form of natural language prompts. Ferret [52] proposes a spatial-aware visual sampler that enables the arbitrary shapes of visual prompts. The current proposed Osprey [55] unlocked the capability of pixel understanding, alleviating the influence of irrelevant information in the visual prompt inputs. These methods have propelled the REG task into the era of MLLMs. Considering that MLLMs incorporate extensive knowledge from unimodal and multimodal pre-trained corpora, there is a potential for the REG task to leverage this inherent knowledge within the models to generate more specific and detailed expressions without additional training, thus more effectively addressing real-world applications [21, 39, 41].

Decoding strategies to mitigate hallucination in Large Language Models. Large language models are pre-trained on unlabeled corpora to acquire extensive world knowledge and subsequently undergo post-training to learn to follow instructions [34] and align with human preferences [2]. This systematic pre- and post-training pipeline makes them powerful at solving a wide range of NLP tasks [16, 36, 40, 43]. However, some studies indicate that they may fail to accurately assess their own knowledge [51] and often exhibit overconfidence in their responses [47], which results in hallucinations [59]. To mitigate these issues, recent research [5, 8, 20] proposes inference-time decoding strategies for trained LLMs to find latent knowledge inside the internal activations without additional training. C. Burns et al. [5] introduce a Contrast-Consistent Search (CCS) algorithm to identify a direction of truth in the activation space of LLMs that remains consistent across negations, thereby reducing generated errors. Based on the discovery of CCS, ITI [20] dives deep into attention heads and suggests shifting model activations alongside factuality-related heads during inference to help reduce hallucinations. Besides 'finding the direction of truth,' DoLa [8] proposes contrastive decoding by comparing the differences in logits between the projections of later and earlier layers to better surface factual knowledge and reduce the generation of incorrect facts. In line with the motivations of [5, 20], our investigation uncovers that well-trained MLLMs' intermediate layers differ in multi-modal alignment and region-level understanding capabilities. These observations inspire us to devise an inference-time decoding strategy that combines latent knowledge from multiple layers (with prior importance) to alleviate hallucinations.

Hallucination in Multi-modal Large Language Models. In the realm of MLLMs, 'hallucination' typically refers to 'object hallucination,' where the models generate plausible outputs containing objects that are either absent from or mismatched with the images [15, 19, 24, 38, 50], and is commonly categorized into three types: category, attribute and relation hallucinations [3, 49]. Some efforts based on instruction tuning have been made to mitigate this issue in MLLMs. LRV-Instruction [26] introduces a dataset with positive instructions and unique negative prompts with different semantic levels to better align responses with image content. HACL [15] explores the vision-language embedding space, using contrastive learning to separate non-hallucinated from hallucinated texts. Without instruction tuning, Woodpecker [50] offers a training-free pipeline for hallucination correction, using expert models to enrich image context and ensuring each phase is interpretable by a step-by-step correction process. In another line of work, some efforts have sought new decoding strategies to avoid relying on extensive additional data and training: Opera [14] tackles the partial over-trust issue in decoding by applying a penalty to the model logits during beam-search decoding. VCD [19] links object hallucinations to biases and language priors, contrasting outputs from distorted and original visuals to ensure consistent generation. Compared to existing studies, our method initially enriches region-level context from intermediate layers rather than external expert models or knowledge base and provides a multi-layer ensembling solution to mitigate hallucinations.

3 Method

3.1 Preliminary

Leveraging the nuanced language representation capabilities inherent in multi-modal large language models, recent investigations have unlocked the REG [6, 37, 52, 55] and RES [18, 37] capabilities of MLLMs. To maintain clarity and conciseness, we have omitted the discussion on instruction tuning and assume that the described tasks are well-instructed by specific prompts. In this work, we utilize a 'mask' as the region prompt.

Referring expression generation. The MLLM-based REG model is typically structured around three principal components: a visual encoder (e.g., CLIP [35] with a linear adapter), a region-encoder,

and an LLM base (e.g., LLaMA [42] or Vicuna [7]). Given an image I , the visual encoder first extracts the visual information, then the linear adapter projects it into I f ∈ R HW × C , where HW denotes the flattened visual token length and C denotes the hidden state dimension of LLM base. A referred region M (presented as a mask) is encoded as M f ∈ R Q × C by region encoder (e.g., a CNN-based extractor [55] or an ROI pooling layer [37, 57]), where Q denotes the length of encoded region prompt tokens. Taking the visual feature I f , regional feature M f , and an embedded instruction text X f ∈ R L × C as input, where L represents the length of the instruction in tokens, the model concatenates them and forms a multi-modal input sequence S ∈ R ( HW + Q + L ) × C .

Regarding the core architecture of the LLM base, the multi-modal sequence S is successively processed by the N stacked transformer layers. Eventually, an affine layer ϕ ( · ) serves as a language model head to predict the probability of the next token y t over the vocabulary set V . The logits for the token y t , given the sequence S and all preceding tokens y <t , are computed as follows:

where h ( N ) t denotes the hidden state of the last transformer layer. The probability of the next token y t is then given by:

Through this process, the model autoregressively generates the output text Y as the region description. For simplicity, we use p ( y t | y <t ) to represent p ( y t | S, y <t ) , and logit N ( y t | y <t ) to represent logit N ( y t | S, y <t ) in the following.

Referring expression segmentation. Recent advances in MLLM-based RES models [18, 37] largely employ a similar MLLM architecture (e.g., LLaVA [27]) as used in the REG models, with the distinction that its input contains a description targeted at a specific region, while its output is a mask that can cover the described object.

A widely adopted strategy [17, 18, 37] incorporates a [SEG] token, enabling the model to identify the [SEG] token in the output sequence as a cue for the presence of a segmentation target. A specialized MLP head ψ processes the output embedding of the [SEG] token h seg ∈ R 1 × C , mapping it into the prompt space of the segmentation fundamental model (e.g., SAM [17]), represented as ˜ h seg = ψ ( h seg ) . The segmentation model then decodes the target mask M s from the query token ˜ h seg and provides its confidence score CF .

3.2 The intermediate layer contains descriptive information

This section focuses on our observations of intermediate layers, attempting to uncover the latent descriptive information. We adopted a region understanding model Osprey-7b [55] with N = 32 layers as REG model, and GLaMM [37] as RES model by default.

Each layer has different generation tendency. To reveal the latent information, we adopt the early-exit strategy on the REG model to generate a series of output sequences by applying the language model head (an affine layer) to the hidden states of each layer. As illustrated in Figure 2, the sequences from the early layers (layers 1 to 10) manifest as nonsensical strings of characters, indicating the suboptimal alignment between the hidden representation of shallow layers and the ultimate vocabulary space of MLLM. Interestingly, among the middle layers (about layers 20 to 25), the model begins to output sentences with semantic meaning and gradually some region-specific expressions appear. Compared with the output of the last layer, these sentences with richer semantic information can sometimes provide more discriminative descriptions for the referred object, e.g. indicating the related position between the target object and others, and/or containing high granularity of attributes. The uniqueness and descriptiveness of expressions given by the middle layers are strongly related to the objective of REG, thus the latent features of intermediate layers have notable potential to enhance the unambiguity in MLLM-based REG.

Potential of intermediate layers. The above observations demonstrate that different layers' understanding of the region context varies. We attempted to visualize the region-aware comprehension capabilities among the layers through two approaches. (a) With the assistance of the RES model: we randomly extracted K = 2000 samples from the RefCOCOg training set to form the triplets ( I , M , Y ) , which corresponds to the entire images I , the target regions represented by masks M , and the descriptions of regions Y . ( I , Y ) are sent into an MLLM-based RES model, to harvest the projected features of [SEG] token ˜ h seg . Subsequently, ( I , M ) are input into the REG model, and the

Visual

Token

Mask

Token

Text

Token

……

……

Layer Output:

...

...

jap to is h

dátum asser

by for to

Ћ

vie

!

is l in

green plant

whichbetwe

en two other

similar ones

green plant

behind chair

occupied by

woman

a plant in a

planter in

the fore-

ground

the tallest

tree in the

middle

Архив

im for

Hinweisberr

ygen

pickering

1st layer

2nd layer

20th layer

22nd layer

30th layer

32nd layer

green plant

which between

two other

similar ones

120

08

Wasserstein-2 distance between [last]&{mask]

110

60

70

100

50

90

30

80

20

70

10

60

2

23 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Layer Index

hidden states of the last token of each layer { h i } N i =1 are obtained. Inspired by FID metric [13] and recent investigations on the latent space communication [30, 32], we first performed PCA dimensionality reduction to project both features into the same dimension, then treat the dimension-reduced [SEG] features as the anchor to calculate the Wasserstein-2 distances [13, 44, 45] between [SEG] and each intermediate hidden states. This allowed us to estimate the relative region understanding ability of each layer for the given multi-modal context of a certain object. We then calculate the frequency with which each layer had the smallest Wasserstein-2 distance to the [SEG] token. As depicted in the right of Figure 2 (orange), except for the final layer, the intermediate layers also have the potential to contain better latent information for a more precise region-related description. (b) Solely within the REG model: we investigated the multi-modal alignment process in region-level context across intermediate layers by calculating the Wasserstein-2 distance between each layer's region-encoded token [mask] and the last language token. Results are reported in Figure 2 (blue). These analyses within a well-trained MLLM show that the tokens of different modalities do not approach monotonically during inter-layer transitions. Hence we should give more chances to the intermediate layers for better region-level understanding. Detailed information can be found in the Appendix C.

3.3 Unleash then eliminate decoding strategy

To enhance the granularity of region-level descriptions without introducing excessive hallucinations, we propose a method that integrates contrastive decoding with cycle consistency-based ranking to screen out appropriate descriptions for the interested regions (Section 3.3.1 and 3.3.2). This approach enables us to specifically leverage the commonly overlooked latent information contained in intermediate layers and ensures the identifiable description through caption quality estimation. Under the concerns of computation efficiency, we further develop a decoding strategy to reduce the operations of cycle ranking through hybrid layer importance measurement (Section 3.3.3). It involves two kinds of layer importance calculation to influence the selection probability of candidate layers during each word prediction step. The harvest prior importance weights can be directly applied to the decoding process of the original MLLM, mitigating the hallucinations. The following subsections provide further details.

3.3.1 Unleash intermediate information by contrastive decoding

As depicted in Figure 2, the manifestation of region-aware information differs across layers. To underscore descriptions related to specific regions-evident in the intermediate layers but faded in the final layer, we adopt a contrastive decoding approach [8, 22] by subtracting the log probabilities of the next token produced by the intermediate layer from those of the final layer. The resulting distribution is defined as the contrastive decoding logits of specific subtractor layers. These logits instead of originally the logits from the final layer are used for generating the subsequent token. Concretely, given a set of candidate layer indices J = { 1 , . . . , n } , the probability of token y t for layer j ∈ J is:

1st layer

32nd layer

...

...

Candidate layers

Contrastive Decoding

LLM

[

SEG

]

Mask

Decoder

Candidate

Ranking

Comparison

IoU1 IoU2 IoU3

Caption 1

Caption 3

M'

MASK

REG

RES

couch facing

the fire place

empty black

leather chair with

a wood frame

the cozy

reading

chair

Caption 2

where logit j ( y t | y <t ) = ϕ ( h ( j ) t ) , V head ( y t ) = { y i ∈ V : p ( y i | y <t ) ≥ α max w ∈V p ( w | y <t ) } , α ∈ [0 , 1] is a cutoff hyperparameter that truncates the next token distribution p ( y t | y <t ) of the final layer. Following the previous works [8, 22], we set α = 0 . 1 in the implementation.

After contrastive decoding, each of the intermediate layers in the candidate set suggests a probability for predicting the next token y t . Finally, we harvest a sentence set A = { a 1 , a 2 , ..., a n } whose size equals to the size of candidate layers.

3.3.2 Cycle-consistency-based intermediate sentence quality ranking

Consider the formed triplet ( I , M , Y ) in Section 3.2, where I serves as the global contextual background of a region, M and Y respectively represent two modalities of the same object. For a pair of ideal REG and RES models, it is anticipated that M and Y can be interconverted losslessly during a cyclic operation of these two models. This implies that, due to the cycle consistency between the two modalities of the same object, feeding the output of the REG model into the RES model should yield a mask consistent with the input to the REG. If the output generated from the candidate layer is overly ambiguous or polluted by hallucinations, the RES model may struggle with accurately locating the target object against the background. Based on these assumptions and observations, we utilize RES model [37] to estimate the region understanding performance of the captions generated by the candidate layers, allowing us to rank these candidates effectively.

Figure 3 illustrates the pipeline of cycle-consistency-based quality ranking. The input image I and the referred region (represented as a mask) M are processed by the REG model, which continuously extracts and aligns features across successive layers for the multi-modal context. After the information elicitation of intermediate layers, we harvest a set of sequence A = { a 1 , a 2 , ..., a n } . Each sequence in A , paired with the image I , forms an input pair ( I, a j ) to feed into the RES model. The RES model then segments out the corresponding mask M ′ j for each input pair.

We evaluate the quality of each layer's sentences by calculating the Intersection over Union (IoU) between M and M ′ j :

where CF j refers to the output of IoU score head within the segmentation foundation model (e.g. SAM [17]), which can be interpreted as a confidence score of the generated mask. Subsequently, the candidate sentences from each layer are ranked by the Score j . For each sample, the a j with the highest Score j is selected as the final sentence, and its layer is deemed the best candidate layer.

Algorithm 1 Layer Prior Importance Calculation

3.3.3 Hybrid layer importance measurement

Although our cycle-consistency-based candidate ranking process improves the generation quality, it introduces additional computational load from the RES model compared to the original decoding method, significantly affecting the per-sample decoding speed. To alleviate this issue, we propose a simple yet effective strategy called Probing-based Importance Estimation to speed up the decoding process. This strategy involves frequency counting of each candidate layer being the optimal layer based on their performance within a probing subset. With a subset containing m samples, the specifics of this calculation are outlined at Algorithm 1. The estimated weight q is then served as prior knowledge that reflects the candidate layers' importance and is then utilized over the entire dataset in the decoding process, intervening in the next-word prediction.

Furthermore, inspired by the success of distance-guided layer selection in LLMs [8], we also apply this metric as a second guidance. Concretely, we first calculate the distance between the next-token probability of the final layer and each candidate layer at the current decoding step, then the calculated values are normalized across candidate layers as follows,

where D j = JSD ( p || p con j ) denotes the J-S divergence between the next-token probability of the final layer p and the layer p con j . Finally, the hybrid layer importance is obtained by adding up the probing-based prior and sample-wise distance followed by a softmax normalization:

where d is a vector of normalized divergence values across the candidate layers. We then sample from the probability distribution ˜ q to select one layer among candidates to predict the next token at each decoding step, till the generation finishes.

Overall, this decoding approach first estimates a prior layer-wise importance weight from a small subset and then applies this distribution to contrastive decoding, effectively improving decoding efficiency while preserving the ability to mitigate hallucinations. In addition, our experiments (Table 3) also show that the prior importance q calculated from one (probing) dataset could be directly transferred to another dataset with similar image domain for reducing the hallucinations. This strong transferability further illuminates a new promising application scenario where the historic estimate could serve as a cold-start for inference in new environments when the probing dataset is not available.

4 Experiments

4.1 Datasets and metrics

RefCOCOg. The RefCOCOg [33] dataset is a classical benchmark for referring expression generation, which contains 85,474 expressions for 54,822 objects in 26,711 images. Expressions in RefCOCOg are annotated on Amazon Mechanical Turk in a non-competitive way and tend to be longer (8.43 words per sentence on average) and more expressive than RefCOCO [53] and RefCOCO+ [53]. In the MLLM-empowered REG task, the relatively short ground truth cannot fully cover the expression space of the sentences generated by the MLLM at evaluation. Therefore, our experiments focus on the METEOR metric as it is more comprehensive and flexible, allowing for a more nuanced recognition of linguistic variations.

CHAIR Evaluation on Hallucinations . The Caption Hallucination Assessment with Image Relevance (CHAIR) metric [38] is commonly used to evaluate object hallucinations that occur in image description tasks. It comprises two distinct assessment dimensions, including CHAIR S that calculates on sentence-level and CHAIR I that calculates on a more granular object-level. We observe that CHAIR only counts hallucinated objects for each 'central object', which means that if the model wants to enrich the semantic information and generate new tokens, it will face the risk of increasing hallucinations compared to the shorter-sentence models. This undermines the comparability of this metric across sentences of varying lengths. Hence we propose normalized CHAIR (nCHAIR) based on CHAIR to conduct a fair comparison between sentences of different lengths. The calculation of nCHAIR S is specified as the following formula, with nCHAIR I calculated similarly:

Prompted Visual Hallucination Evaluation Benchmark (PHD) . This benchmark [28] focuses on the four major types of hallucination faced by MLLMs, namely Object hallucination, Attribute hallucination, Multi-modal conflicting hallucination, and Counter-common-sense hallucination. It evaluates and explores the hallucinations through comprehensive prompt-based tasks, which also helps identify the causes of these hallucinations. While this benchmark does not explicitly evaluate regionlevel hallucinations, its detailed evaluating strategies for object attribute and position are closely related to region-level understanding, allowing it to effectively indicate regional-level hallucinations.

4.2 Main result on referring expression generation

Baseline region-level MLLM model and decoding method. Table 1 presents the performance comparison of semantic quality and hallucination evaluation on RefCOCOg dataset, with METEOR and CHAIR metrics. Recent research [58] has revealed that the temperature parameter t has a notable effect on the hallucination of generated sentences. In light of this, we included an analysis of the baseline region-level MLLM model, Osprey-7b [55], performing at both lower ( t = 0 . 2 ) and higher ( t = 0 . 9 ) temperature settings. Meanwhile, we also compared a baseline decoding method, DoLa [8], which demonstrated that leveraging contrastive decoding in vanilla LLMs can enhance the authenticity of the generated results. In comparison to the Osprey-7b 1 , the sentences generated by DoLa [8] demonstrated better performance on METEOR and nCHAIR metrics.

Performance of cycle-consistency-based sentence quality ranking. As introduced in Section 3.3.2, to balance the trade-off between information granularity and accuracy, we first unleashed the region description of intermediate layers by contrastive decoding, then filtered out the inaccurate sentences by cycle-consistency-based quality ranking. For each sample in RefCOCOg, we evaluated the best sentence generated by the best candidate layer. The result is reported in the fifth row of Table 1. We observed that our method not only gains more descriptive sentences but also demonstrates a reduction in the hallucination metric (measured in terms of nCHAIR) compared to Osprey-7b and DoLa. The examples of generation results are listed in Figure 4.

Text generation via hybrid layer importance measurement. In Section 3.3.3, we proposed a method that effectively reduces the computational overhead of the RES scoring model. In our experiment, we divided all 32 intermediate layers (including the embedding layer) into four consecutive groups (detailed in Section 4.3) and calculated the relative importance of the layers within each group

Dola: A brown wooden table sits in  the  foreground  of  the  photo, with a baby sitting on it.

Ours: A  large  brown  wooden dining  room  table  dominates  the scene.  The  table  is  littered  with various  objects  including  a  bowl with chips, a bottle, a spoon and a cup.

Dola: The right side of the sandwich contains the  deli  meat , cheese, lettuce, tomato, and mustard.

Ours: A piece of bread is visible on  the  right  side  of  a  plate.  This bread appears to be a bun and is positioned in front of some tomatoes.

Dola: A pizza with spinach and tomatoes is being made on a tray. One man is holding a slice of the pizza while the other adds toppings

Ours: There  is  a  pizza  on  the left,  being  picked  up.  The  pizza appears to have spinach on it, and is topped with tomatoes

Dola: The woman on the left is wearing a plaid shirt and a fringe skirt.  She  has  a  large belt  buckle and a necklace .

Ours: The  woman  on  the  left has long dark hair and is wearing a  plaid  shirt.  She's  standing  next to  the  motorcycle  and  appears  to be a posed model.

Figure 4: The visualization comparison of generations between Osprey-7b [55], DoLa [8] and ours. Osprey-7b's outputs are quite brief and omit visual details of the referred objects. The generations from DoLa are more extensive but are accompanied by hallucinations. In contrast, our method increases descriptive information and curtails the generation of hallucinations.

separately. Figure 5 depicts the resulting layer importance weights of four different ranges. The result reveals that layer importance is not uniform across candidate layers and also varies among different groups. We applied the importance weights estimated from 1/8 subset to the decoding process and listed the first group's result in the fourth row of Table 1. Notably, in comparison to full-set sentence-by-sentence quality ranking, this probing-based decoding method offers comparable performance with improved efficiency, demonstrating the effectiveness of our statistical layer importance estimation.

4.3 Performance of different candidate layer groups

As aforementioned in Section 3.2, it was observed that different layers have distinct generation preferences for the same sample, inspiring our further exploration in quantitative experiments. The first 32 layers (where layer 0 is the embedding layer) of the Osprey-7b model were organized into four groups: [0, 7], [8, 15], [16, 23], and [24, 31]. In each experiment, one group was selected as the candidate layer set and contrasted with the last layer to generate the candidate sentences, followed by cycle-consistency-based ranking to choose the best. Table 2 indicates that the first group exhibits the best performance in METEOR and nCHAIR I metrics. It is also noticeable that there are considerable performance discrepancies between the different groups, with a general trend of semantic quality declining as the depth of the layer increases (after contrastive decoding). However, we found that although the fourth group had the lowest METEOR score, its nCHAIR S was slightly better than that of the first group. This observation suggests that a well-trained MLLM exhibits variations in latent knowledge across its zones, leading to differences in generation performance.

4.4 Transferability of layer prior importance weights

From the preceding experiments, we discovered that using layer importance in the next token prediction enhances the generated output's quality of the same dataset. Additionally, we also noticed that the determined layer prior importance can be smoothly transferred to a different dataset (different prompts) sharing a similar image domain to reduce the hallucinatory outputs. Specifically, we applied the layer importance ([0, 7] group) calculated on the RefCOCOg dataset directly to the PHD dataset, where the image inputs also originate from MSCOCO [25], and decoded the output using the method described in Section 3.3.3. Given the absence of any region prompt in the PHD dataset, we applied a zero mask to the region encoder. The comparative results are shown in Table 3. We observe that our approach enhances the model's understanding across most tasks, which indicates that without the additional training, the estimated layer prior importance could serve as a cold-start for inference in new environments when the probing dataset is not available, helping reduce the occurrence of hallucinations during the decoding.

5 Conclusion

Our research on the MLLM-based Referring Expression Generation (REG) task explored a potential trade-off between information richness and reliability of the intermediate generation results. We introduced a 'unleash-then-eliminate' approach that utilizes latent information from intermediate layers and employs a cycle-consistency-based decoding method to reduce hallucinations. Our method outperforms existing techniques, confirming its efficacy for enhancing REG task performance.

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Appendix

In this appendix, we present more ablation studies on our proposed decoding strategy, including the impact of different MLLM-based RES models and the influence of subset size in probing-based importance estimation. We also elaborate on the calculation of adopted hallucination metrics. At last, we provide a visualization that uncovers the multi-modal alignment process in the intermediate layers.

A More Ablation Studies

In this section, we provide more ablation studies, including the impact of the different assisting RES models in cycle-consistency-based quality ranking, and the subset size in probing-based estimation. Different scoring RES model . In Table 4, we ablate the impact of different RES models utilized in the proposed cycle-consistency-based quality ranking (CCR). We adopt another MLLM-based RES model LISA [18] to score the sentence quality of intermediate layers. From the table, we can observe that the choice of the RES model affects the CCR. The RES task itself involves transforming a language query into a pixel-level visual representation of an object. Given that GLaMM [37] performs slightly better than LISA [18] in RES, we can therefore infer that the more robust this transformation is completed, the better the performance of the CCR in quality ranking.

Impact of probing subset quality and size . We also conducted ablation experiments on different subset sizes and the randomness of sampling, with the results listed in Table 5. In the first row, we report the average result of 10 different random seeds for 1/8 subset sampling, which is also the result shown in Table 1 of the main paper. The second row reports the best results for the 1/8 subset. The results indicate that the randomness of the sampled subsets affects the performance of our proposed decoding strategy in reducing hallucinations. In other words, the quality of the subset has a certain impact on the decoding outcome.

However, assessing the quality of subsets during probing is also an intractable problem. One possible solution is to use an unsupervised clustering method (e.g., K-means) to first cluster the multi-modal features (extracted by CLIP [35]/embedding layers of MLLM [55]) of the entire dataset, and divide the subsets based on different centroids, then calculate and store the inter-layer weights in an 'importance weight bank.' During inference, we can compute the distance between the new query feature and these centroids, selecting the set of weights from the closest centroid for inter-layer combination during decoding. This strategy needs careful designing, and we consider it a future extension. Besides quality, by comparing the second and third rows, we also find that compared to the 1/8 subset, the 1/16 subset shows less stable de-hallucination and generation performance.

B Hallucination Metrics

The hallucination of objects in MLLM [14, 23, 38] refers to the situations where the descriptions generated by the model do not match the appearance of the object (attributes, relation etc.) in the original image. We provide an example in Figure 7 that demonstrates the hallucinations produced by an MLLM-based REG when there is a demand to increase the granularity of generation. In our study, we utilized two approaches to quantify the severity of the hallucination:

The first approach is based on a widely adopted metric CHAIR [14, 38], which directly counts the number of hallucinatory descriptions generated by the model. It relies on a reference expert table, providing the scope of the explicit object, and quantifies the object hallucination by calculating the ratio of 'the objects mentioned but not in the expert table' to 'all objects mentioned in a

Is the person feeling anxious

in the situation?

No, he seems happy

.

Prompt type: Neutral & Sentiment

Prompt type: Neutral & Attribute

Prompt type: Neutral & Position

Prompt type: Misleading & Sentiment

Is the table made of wood?

No, there is a glass table.

In the image, is a motorcycle

situated in front of the truck?

The people in the image are undoubtedly

feeling nervous. Based on the image, are

the people feeling nervous in the situation?

No, they are not nervous

.

No, there is no motorcycle

.

Prompt type: Misleading & Attribute

The

bowl

has

a

smooth,

lightweight

texture like a disposable container. Based

on the image, is the bowl made of metal?

Yes, it's a stainless steel bowl

.

Prompt type: Misleading & Position

The object to the right of the TV is used

for carrying items. Based on the image, is

the toilet to the right of the tv?

Yes, the toilet is to the right of the TV

.

description'. CHAIR divides the hallucination into two levels. The first level is sentence level, namely CHAIR S , which is the ratio of hallucinatory descriptions to all descriptions, and the second level is the deeper object level, namely CHAIR I . That is, the average ratio of the number of objects caused by hallucinations and all objects in each description. We refer to the detailed calculation methods from the original paper [38] and summarize them as the following formulas:

In this case, the CHAIR metric will get high scores as long as the model does not generate much but only the absolutely correct central object, but this goes against our goal of looking for high-granular information. Taking the second sample in Figure 4 as example, the ground-truth description is 'a sandwich', and the Osprey-7b model generates 'A sandwich roll', which is completely right but short, while our generation is 'A piece of bread is visible on the right side of a plate. This bread appears to be a bun and is positioned in front of some tomatoes.' Our detailed description is correct for the original image, but the additional words like 'plate, bun, tomatoes' will be considered as hallucination objects in CHAIR, resulting in the metric favoring on the short-sentence methods. Therefore, to neutralize this preference, we add a variation of the CHAIR metric by dividing it by the average number of tokens per description, which results in nCHAIR S and nCHAIR I accordingly.

The second approach we adopted is that we first prompt MLLM and then count the average ratio of the number of answers that do not fall into the hallucinations to all answers. We utilize the PHD benchmark [28] to achieve this. This benchmark sets ten different types of questions, which are composed of five different tasks, with each task featuring two modes of questioning. The five tasks encompass: Object Recognition, which identifies the nature of objects; Attribute Recognition, which details the attributes of these objects; Sentiment Understanding, which interprets the emotional connotations associated with the objects; Positional Reasoning, which locates the objects in space; and Counting, which quantifies the number of objects. The two modes of questioning include Neutral

mode and Misleading mode . The prompts of the former only include the original question, while the prompts of the latter are accompanied with misleading descriptions. Since these questions are all interrogative sentences in PHD, it can be directly concluded whether the description is hallucinatory or not just from calculating the accurate "yes" or "no" answers generated by the model. The prompt and response examples are listed in Figure 6.

工

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C Visualization of intermediate multi-modal alignment

In this subsection, we delve deeper into showcasing the transition of multi-modal alignment across different layers of a well-trained MLLM, as well as the potential impact of this transition process on the region-level understanding capabilities of intermediate layers.

Similar to Section 3.2, we considered 2000 triplets ( I , M , Y ) based on a pair of RES and REG models. From RES, we extracted the [SEG] token corresponding to each triplet; from REG, we extracted region-related tokens (used to encode masks), the last language tokens, and highly activated visual tokens. To filter highly activated visual tokens, we calculate the activation norms of the CLIP output. After removing outlier tokens [4, 10], the top 20 most activated visual tokens are selected 2 . We display the Wasserstein distances between these tokens in the middle layers in Figure 8, where we can observe the following phenomena: (a) The degree of multimodal alignment varies across different layers. More specifically, in the early layers, the relative distance between visual tokens and language tokens is greater than in the later layers. (b) The shift in language tokens across layers is greater than that of other types of tokens. (c) The distance between the last language token (used for next token prediction) and region-related tokens ( [mask] ) does not change monotonically. Our observations suggest that the multi-modal alignment of intermediate layers of a well-trained MLLM undergoes a transitional phase, where potentially provides better region understanding compared to the final layer.

D Limitations

Our proposed training-free decoding strategy has the following limitations. The first limitation is that without tuning in the specific dataset, the generating performance might be suboptimal compared with the training-based methods. Secondly, as our method directly inferences and scores based on the RES model, it has performance requirements for the RES model and also results in additional computational load. Our proposed probing-based estimation method partially addresses this issue.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the Multi-modal Large Language Model (MLLM) based Referring Expression Generation (REG) task, which aims to generate unambiguous text descriptions for specific objects or regions in images. MLLM-based REG models tend to suffer from hallucination issues, and there is a trade-off between detailed descriptions and accurate targeting. To address this, a training-free method called \"unleash-then-eliminate\" is proposed, which elicits latent information in intermediate layers and uses cycle-consistency decoding to reduce hallucinations. Extensive experiments on the RefCOCOg and PHD benchmarks show that this method outperforms existing approaches in both semantic and hallucination-related metrics.\n\nSTRENGTHS:\n1. The observation that the intermediate layers of the current region-level MLLMs sometimes hold more descriptive regional information than the final layer is interesting.\n2. The writing throughout the paper is clear and easy to follow. The authors have done a good job in presenting their ideas and methodologies in a manner that is both logical and comprehensible.\n3. The experimental results are compelling and demonstrate that the proposed method outperforms existing methods on the newly introduced evaluation metric.\n\nWEAKNESSES:\n1. Since the proposed method requires multiple layers for inference to be described, both REG and RES models are required, resulting in a very low efficiency of the method and requiring a lot of additional calculations and memory.\n2.  The ablation study is not sufficient. For example, subset used to select the optimal layer in Layer Importance Measurement. What is the Impact of subset size and quality on performance? What is the performance of using  cycle-consistency-based candidate ranking process for whole dataset, i.e., not using Layer Importance Measurement.\n3.  What is effect of using different RES models for the proposed method.\n\nQUESTIONS:\n1. What is the effect of \\alpha defined in Line 172\n2. \\mathcal{V} is not defined in Line 171.\n3.  The candidate sentence set A use n to represent  the size of candidate layers, while use N-1 in Line 169. So are n and N-1 the same, or do they represent different things?\n\nLIMITATIONS:\nThe authors adequately addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an approach for improving the accuracy and richness of referring expression generation (REG) by leveraging the descriptive potential of intermediate layers in Multi-modal Large Language Models (MLLMs). The method employs a cycle-consistency-based decoding strategy to reduce hallucinations and improve descriptive quality. The proposed method is evaluated on the RefCOCOg and PHD benchmarks, demonstrating superior performance over existing methods.\n\nSTRENGTHS:\nThe \"unleash-then-eliminate\" strategy and the use of intermediate layers for generating more detailed descriptions is innovative to me.\n\nWEAKNESSES:\nThe proposed method introduces additional complexity, particularly in the decoding process. While effective, the cycle-consistency-based approach may increase computational overhead, which could limit its applicability in real-time or resource-constrained environments.\n\nThe related work section lacks depth in its analysis and could benefit from a more thorough review of recent advancements, particularly in hallucination mitigation techniques.\n\nSome terms and notations, such as Q, H and W in Section 3.1 , are not clearly defined in the context of the paper, which can lead to confusion. Providing explicit definitions and clarifications would improve readability.\n\nQUESTIONS:\nYou use PCA for dimensionality reduction and Wasserstein distances for comparing features. Can you elaborate on why these specific techniques were chosen and how they contribute to the effectiveness of your method compared to other possible choices?\n\nYour method aims to reduce hallucinations in generated descriptions. Can you provide more details on how you quantify hallucinations and the specific improvements observed with your method compared to baselines? Are there any cases where your method still struggles with hallucinations?\n\nIs there any Transformer-based region encoder?\n\nLIMITATIONS:\nThe authors have not adequately addressed the limitations and potential negative societal impacts of their work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper aims to strike a balance between detailed description and precise captioning when using multimodal large language models (MLLM) in the task of referring expression generation (REG). A key observation is that the output of a Referring Expression Segmentation (RES) model should be consistent with the input of a REG model.\n\nBased on this observation, a training-free method has been proposed, called 'unleash-then-eliminate,'  which adopts an 'elicit-then-eliminate decoding' strategy. Captions are generated using contrastive decoding (Li et al., arXiv:2210.15097) and then fed into a RES model to produce corresponding masks. The Intersection over Union (IoU) between the masks tagged by the RES model and the input of the REG model is leveraged to select the candidate layer along with the generated caption. Additionally, a Probing-based Importance Estimation method is proposed to accelerate the decoding process.\n\nThe generation quality of the model is evaluated using the METEOR score on the RefCOCOg dataset. Additionally, the model's performance in avoiding hallucinations is assessed with the CHAIR and PHD metrics. The model proposed in this paper outperforms both Osprey and Dora not only in terms of generation quality but also in terms of adequacy.\n\nSTRENGTHS:\n1. The problem definition and motivation are clear. The paper indicates that a balance between detailed description and accurate targeting is necessary when using MLLM.\n\n2. The model architecture is clear. The proposed model utilizes contrastive decoding to unleash the information in intermediate layers for generating captions. It indirectly evaluates the quality of these layers by assessing the masks generated by a RES model in relation to the captions. Additionally, a Probing-based Importance Estimation method is proposed to expedite the decoding process.\n\nWEAKNESSES:\n1. The writing is disorganized and lacks clarity. For example, in the first sentence \"Referring expression generation (REG) Yu et al. [2016], Mao et al. [2016], Hu et al. [2016], Yu et al. [2017], Luo et al. [2020], Ding et al. [2021], Tanaka et al. [2019] is a task to\", the reference is mixed with words, making it difficult to follow.\n\n2. The proposed approach is technically simple. The architecture seems simply a combination of REG and RES models.\n\n3. The motivation is to achieve a trade-off between the granularity and accuracy of captioning by selecting different intermediate layers. However, the experiments only show that the proposed model outperforms other models, as indicated in Table 1. Furthermore, Table 2 reveals that the first bucket of layers achieves the best scores in both METEOR, which relates to generation quality, and nCHAIR_I, which relates to the avoidance of hallucinations. This finding contrasts with the initial proposal.\n\n4. The answer to `Open access to data and code` is [Yes], but there's only a `placeholder` found in the anonymous repo,  noted as footprint in the first page of paper.\n\nQUESTIONS:\n1. What is the final architecture of the proposed model with `Probing-based Importance Estimation` ?\n\n2. What are `the number of parameters` of the proposed models (with and without Probing-based Importance Estimation)?\n\n3. What are the metrics corresponding to those types (i.e. Object Recognition...) in table 3? What is the difference between `neutral-question mode` and `misleading mode`?\n\nLIMITATIONS:\nYes.\nThe authors claim that since the model has not been tuned on a specific dataset, the generating performance is suboptimal compared to training-based methods.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the Referring Expression Generation (REG) task using Multi-modal Large Language Models (MLLMs), identifying the key challenge as the trade-off between generating detailed descriptions and accurately targeting the referring objects, which often leads to hallucinations—the inclusion of incorrect or spurious information in the generated text. To address this issue, the authors propose a training-free \"unleash-then-eliminate\" method that leverages the intermediate layers of MLLMs and employs a cycle-consistency-based decoding strategy to mitigate hallucinations. The proposed approach is validated through extensive experiments on the RefCOCOg and PHD benchmarks, demonstrating superior performance compared to existing techniques.\n\nSTRENGTHS:\n1. The paper is in well-written, which makes it easy to understand.\n\n2. The proposed \"unleash - then - eliminate\" method is training-free, which can avoid the need for additional data and training, reducing the complexity and cost of the model.\n\n3. I like the idea of utilizing the latent information in the intermediate layers of the current region-level MLLMs, which is often overlooked but contains more descriptive regional information.\n\n4. The cycle-consistency-based decoding method helps to alleviate the production of hallucinations in the generated sentences, improving the accuracy and reliability of the model's output. The hybrid layer importance measurement strategy not only increases the decoding speed but also maintains the ability to mitigate hallucinations, achieving a good balance between efficiency and performance.\n\n5. The method shows superior performance compared to existing methods on both semantic and hallucination - related metrics in the experiments, demonstrating its effectiveness.\n\nWEAKNESSES:\n1. Without tuning in the specific dataset, the generating performance of the method might be suboptimal compared to the training - based methods.\n\n2. The methods used in the paper, such as cycle ranking, may introduce additional computational load, which could affect the per-sample decoding speed. Although the proposed strategy helps to alleviate this issue, it may not completely solve it.\n\n3. The method assumes that the RES model can accurately estimate the region-aware descriptive performance of the captions generated by the candidate layers. However, the RES model may also have its own limitations and errors, which could affect the final evaluation results.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nrefer to weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}